Tree phenology analysis with R
Ricarda Scholte-Reh
2025-03-04
This logbook will answer the Exercises of the module “Tree phenology
analysis in R”. It will start with chapter 3, ends with chapter 35 and
will only shortly tell if a chapter is left out (like chapter 17).
Chapter 03 Tree dormancy
Put yourself in the place of a breeder who wants to calculate the temperature requirements of a newly released cultivar. Which method will you use to calculate the chilling and forcing periods?
- Because the cultivar is already released there should be official
data about the cultivar. Therefore I would use these data to calculate
the temperature requirements. As base data I need the long phenological
data sets and temperature records. With these I can calculate finally
the date of dormancy overcome.
- Because the cultivar is already released there should be official
data about the cultivar. Therefore I would use these data to calculate
the temperature requirements. As base data I need the long phenological
data sets and temperature records. With these I can calculate finally
the date of dormancy overcome.
Which are the advantages (name 2) of the BBCH scale compared with early scales?
Growth stages are easily recognizable under field conditions.
Growth stages are graded in the order of appearance.
Classify the following phenological stages of sweet cherry according to the BBCH scale.
The pictures are described from left to right. All of the classifications are based on the given pictures and I assume that the image section shown corresponds to the most represented of the tree.
- Picture 1: BBCH-stage 03
- Main stage: 00: sprouting
- Stage 03: end of the bud swelling (leave buds), parts of the buds are light green, the bud scales are spread apart
- Picture 2: BBCH-stage 65
- Main stage 60: Flowering
- Stage 65: Full flowering, at least 50 % of the flowers are open, the first petals are already falling.
- Picture 3: BBCH-stage: 86
- Main stage 80: ripening or maturity
- Stage 85: Coloring advanced (The color is too light to be ripe but is clearly advanced).
- Picture 1: BBCH-stage 03
Picture 01: Phenological stages
of a cherry tree
Chapter 04 Climate change and impact projection
- List the main drivers of climate change at the decade to century
scale, and briefly explain the mechanism through which the currently
most important driver affects our climate.
- Main drivers of climate change (influence from decade to century
scale)
- Greenhouse gases
- Aerosols
- Clouds
- Surface albedo
- Ozone
- Sun
- Long-term drivers
- Currently most important for the climate are greenhouse gases and
their increasing amount in the atmosphere
The reason are mainly human activities like deforestation, fossil fuel combustion and industrial processes
Greenhouse gases trap heat in the atmosphere
Can absorb only radiation of certain wavelengths
Short-wave radiation from Earth surface is absorbed
- Main drivers of climate change (influence from decade to century
scale)
- Explain briefly what is special about temperature dynamics of recent
decades, and why we have good reasons to be concerned.
Recently the mean temperatures are globally rising clearly and very drastically
Especially in the last 50 to 100 years
This an other factors have a great influence in our climate and weather (regional and global)
We will and already do experience more extreme weather (like more dry periods, extreme rain, especially mild and extreme winter)
Most important is the rapid warming (as conclusion)
- What does the abbreviation ‘RCP’ stand for, how are RCPs defined,
and what is their role in projecting future climates?
RCP -> Representative Concentration Pathways
RCPs project future greenhouse gas concentrations (NOT emissions) based on different climate change scenarios until the year 2100
The different scenarios are labeled after the possible radioative forcing values in the year 2100
- Briefly describe the 4 climate impact projection methods described
in the fourth video.
- Statistical models
- Analyzes relationships between species occurrences and environmental factors to predict distributions. Often based on historical data.
- Does not account for ecological interactions or future adaptability.
- Species distribution modeling
- Uses species presence/absence data and environmental variables to estimate potential distributions under current and future climates.
- Assumes species are in equilibrium with their environment, which may hold under climate change.
- Process-based models.
- Simulate biological and ecological processes (e.g. metabolism, reproduction) to predict species and ecosystem responses.
- Requires extensive species-specific data and is computationally demanding.
- Climate analogue analysis
Identifies regions where current conditions resemble projected future climates to predict potential species shifts.
Does not consider non-climatic factors (e.g. land use, human impact) that influence species survival.
- Statistical models
Chapter 05 Winter chill projections
- Sketch out three data access and processing challenges that had to
be overcome in order to produce chill projections with state-of-the-art
methodology.
- Climate Data Acquisition
- Climate datasets come from multiple sources, such as meteorological stations, reanalysis datasets and climate models.
- Differences in spatial and temporal resolution require preprocessing to ensure consistency.
- Missing data and measurement errors need handling to avoid distortions in projections.
- Model Selection and Calibration
- Multiple winter chill models exist (e.g. Chill Hours, Utah Model, Dynamic Model) each responding differently to temperature variations.
- Choosing the most appropriate model for a specific region is crucial, as different tree species have varying chill requirements.
- Calibration is needed to align models with observed historical data and improve predictive accuracy.
- Uncertainty and Bias Correction
Climate models often introduce biases that must be corrected before using their projections.
Different greenhouse gas emission scenarios (e.g. RCP2.6, RCP4.5, RCP8.5) result in a range of possible future conditions, adding uncertainty.
Statistical methods, such as ensemble modeling and probabilistic approaches, help quantify and communicate these uncertainties.
- Climate Data Acquisition
- Outline, in your understanding, the basic steps that are necessary
to make such projections.
Data collection and Preprocessing
- Gather historical temperature records and future climate projections from global and regional sources.
- Standardize the data sets by converting them into compatible formats and resolutions.
- Fill in missing data and remove inconsistencies that could affect model accuracy.
Chill Model Application
- Apply a chosen winter chill model to calculate chill accumulation over time.
- Compare results from different models to understand their variability and sensitivity.
- Validate model outputs against historical observations to ensure reliability.
Climate Scenario Analysis
- Run chill models using projected temperature data under different climate change scenarios.
- Analyse how winter chill accumulation might shift in various regions over time.
- Compare results across different periods (e.g. near-future vs. late-century) to assess long-term trends.
Validation and Uncertainty Assessment
Compare modeled projections with historical observations to evaluate accuracy.
Apply statistical techniques to quantify uncertainties and assess model reliability.
Use ensemble approaches to minimize errors and account for variability between models.
Visualization and Interpretation
Present results using graphs, maps, and reports for easy interpretation.
Provide insights for stakeholders, such as farmers and policymakers, to support decision-making.
Communicate potential risks and adaptation strategies based on projected changes.
Chapter 06 Manual chill analysis
Write a basic function that calculates warm hours (>25° C).
Basic structure:
# read in Dataset (e.g. Winters_hours_gaps)
dataset[ , new_column_name] <- dataset$temperature_column > 25
Apply this function to the Winters_hours_gaps dataset.
Winters_hours_gaps <- Winters_hours_gaps
hourtemps <- Winters_hours_gaps[ , c("Year", "Month", "Day", "Hour", "Temp")]
hourtemps[ , "warm_hour"] <- hourtemps$Temp > 25
hourtemps[2503:2507, ]## Year Month Day Hour Temp warm_hour
## 2503 2008 6 15 16 27.604 TRUE
## 2504 2008 6 15 17 26.989 TRUE
## 2505 2008 6 15 18 24.436 FALSE
## 2506 2008 6 15 19 23.088 FALSE
## 2507 2008 6 15 20 19.555 FALSE- Extend this function, so that it can take start and end dates as inputs and sums up warm hours between these dates.
Start_Date <- which(hourtemps$Year == 2008 &
hourtemps$Month == 6 &
hourtemps$Day == 1 &
hourtemps$Hour == 12)
End_date <- which(hourtemps$Year == 2008 &
hourtemps$Month == 6 &
hourtemps$Day == 30 &
hourtemps$Hour ==12)
sum(hourtemps$warm_hour[Start_Date:End_date])## [1] 250# The result is 250 and will be the control for the following function with the same start and end
sum_warmH <- function(WHourly,
startYear,
startMonth,
startDay,
startHour,
endYear,
endMonth,
endDay,
endHour)
{WHourly[,"warm_hour2"] <- WHourly$Temp > 25
Start_row <- which(hourtemps$Year == startYear &
hourtemps$Month == startMonth &
hourtemps$Day == startDay &
hourtemps$Hour == startHour)
End_row <- which(hourtemps$Year == endYear &
hourtemps$Month == endMonth &
hourtemps$Day == endDay &
hourtemps$Hour == endHour)
WHs <- sum(WHourly$warm_hour[Start_row:End_row])
return(WHs)}
sum_warmH(hourtemps,
startYear = 2008,
startMonth = 6,
startDay = 1,
startHour = 12,
endYear = 2008,
endMonth = 6,
endDay = 30,
endHour = 12)## [1] 250Chapter 07 Chill models
- Run the “chilling()” function on the Winters_hours_gap dataset.
hourtemps <- Winters_hours_gaps[ , c("Year", "Month", "Day", "Hour", "Temp")]
chilling_l7 <- chilling(make_JDay(hourtemps),
Start_JDay = 90,
End_JDay = 100)
chilling_l7## Season End_year Season_days Data_days Perc_complete Chilling_Hours
## 1 2007/2008 2008 11 11 100 40
## Utah_Model Chill_portions GDH
## 1 15.5 2.009147 2406.52- Create your own temperature-weighting chill model using the “step_model()” function.
df_l7 = data.frame(
lower = c(-1000, 1, 2, 3, 4, 5, 6),
upper = c( 1, 2, 3, 4, 5, 6, 1000),
weight = c( 0, 1, 2, 3, 2, 1, 0))- Run this model on the Winters_hours_gap dataset using the “tempResponse()” function.
step_model_l7 <- step_model(HourTemp=hourtemps$Temp, df=df_l7)
TResp_l7 <- tempResponse(make_JDay(hourtemps),
Start_JDay = 90,
End_JDay = 100,
models = list(Chill_Portions = Dynamic_Model,
GDH = GDH))
TResp_l7## Season End_year Season_days Data_days Perc_complete Chill_Portions GDH
## 1 2007/2008 2008 11 11 100 2.009147 2406.52Chapter 08 Making hourly temperatures
- Choose a location of interest, find out its latitude and produce
plots of daily sunrise, sunset and day length.
- Location of interest: Dairy farm with own fodder cultivation (belongs to my uncle)
- Bislich, 46487 Wesel
- Latitude: 51°40’58.4”N Longitude: 6°29’46.4”E
Days_l8 <- daylength(latitude = 51.4, JDay = 1:365)
Days_df_l8 <- data.frame(JDay = 1:365,
Sunrise = Days_l8$Sunrise,
Sunset = Days_l8$Sunset,
Daylength = Days_l8$Daylength)
Days_df_l8 <- pivot_longer(Days_df_l8, cols=c(Sunrise:Daylength))
ggplot(Days_df_l8, aes(JDay, value)) +
geom_line(lwd = 1.5) +
facet_grid(cols = vars(name)) +
ylab("Time of Day / Daylength [hours]") +
xlab("Day of the year [JDay]") +
theme_bw(base_size = 20)
- Produce an hourly dataset, based on idealized daily curves, for the KA_weather dataset (included in ChillR).
hourtemp_KA <- stack_hourly_temps(KA_weather, latitude=50.4)- Produce empirical temperature curve parameters for the Winters_hours_gaps dataset, and use them to predict hourly values from daily temperatures (this is very similar to the example above, but please make sure you understand what’s going on).
empi_curve_l8 <- Empirical_daily_temperature_curve(Winters_hours_gaps)
ggplot(data = empi_curve_l8[1:96, ], aes(Hour, Prediction_coefficient)) +
geom_line(lwd = 1.3, col = "red") +
facet_grid(rows = vars(Month)) +
xlab("Hour of the day") +
ylab("Prediction coefficient") +
theme_bw(base_size = 20)
coeffs_l8 <- Empirical_daily_temperature_curve(Winters_hours_gaps)
Winters_daily_l8 <- make_all_day_table(Winters_hours_gaps, input_timestep = "hour")
Winter_hours_l8 <- Empirical_hourly_temperatures(Winters_daily_l8, coeffs_l8)
# simplify the data for easier use
Winter_hours_l8 <- Winter_hours_l8[ , c("Year", "Month", "Day", "Hour", "Temp")]
colnames(Winter_hours_l8)[ncol(Winter_hours_l8)] <- "Temp_empirical"
Winters_ideal_l8 <- stack_hourly_temps(Winters_daily_l8, latitude = 38.5)$hourtemps
Winters_ideal_l8 <- Winters_ideal_l8[ , c("Year", "Month", "Day", "Hour", "Temp")]
colnames(Winters_ideal_l8)[ncol(Winters_ideal_l8)] <- "Temp_ideal"
# create the "triangular" data set to compare in the end
Winters_triangle_l8 <- Winters_daily_l8
Winters_triangle_l8[ , "Hour"] <- 0
Winters_triangle_l8$Hour[nrow(Winters_triangle_l8)] <- 23
Winters_triangle_l8[ , "Temp"] <- 0
Winters_triangle_l8 <- make_all_day_table(Winters_triangle_l8, timestep = "hour")
colnames(Winters_triangle_l8)[ncol(Winters_triangle_l8)] <- "Temp_triangular"
# the following loop will fill in the daily Tmin and Tmax values for every hour
for (i in 2:nrow(Winters_triangle_l8))
{if (is.na(Winters_triangle_l8$Tmin[i]))
Winters_triangle_l8$Tmin[i] <- Winters_triangle_l8$Tmin[i - 1]
if (is.na(Winters_triangle_l8$Tmax[i]))
Winters_triangle_l8$Tmax[i] <- Winters_triangle_l8$Tmax[i - 1]}
Winters_triangle_l8$Temp_triangular <- NA
# Tmin is fixated to the 6th hour of each day
Winters_triangle_l8$Temp_triangular[which(Winters_triangle_l8$Hour == 6)] <-
Winters_triangle_l8$Tmin[which(Winters_triangle_l8$Hour == 6)]
# Tmax is fixated to the 18th hour of each day
Winters_triangle_l8$Temp_triangular[which(Winters_triangle_l8$Hour == 18)] <-
Winters_triangle_l8$Tmax[which(Winters_triangle_l8$Hour == 18)]
# the following loop will fill in the gaps between the min and max data in a linear way
Winters_triangle_l8$Temp_triangular <- interpolate_gaps(Winters_triangle_l8$Temp_triangular)$interp
Winters_triangle_l8 <- Winters_triangle_l8[ , c("Year", "Month", "Day", "Hour", "Temp_triangular")]
# merge all created data frames for easy display
Winters_temps_l8 <- merge(Winters_hours_gaps, Winter_hours_l8,
by = c("Year", "Month", "Day", "Hour"))
Winters_temps_l8 <- merge(Winters_temps_l8, Winters_triangle_l8,
by = c("Year", "Month", "Day", "Hour"))
Winters_temps_l8 <- merge(Winters_temps_l8, Winters_ideal_l8,
by = c("Year", "Month", "Day", "Hour"))
# convert the date into R's date format and reorganize the data frame
Winters_temps_l8[, "DATE"] <-
ISOdate(Winters_temps_l8$Year,
Winters_temps_l8$Month,
Winters_temps_l8$Day,
Winters_temps_l8$Hour)
Winters_temps_to_plot_l8 <-
Winters_temps_l8[, c("DATE",
"Temp",
"Temp_empirical",
"Temp_triangular",
"Temp_ideal")]
Winters_temps_to_plot_l8 <- Winters_temps_to_plot_l8[100:200, ]
Winters_temps_to_plot_l8 <- pivot_longer(Winters_temps_to_plot_l8, cols=Temp:Temp_ideal)
colnames(Winters_temps_to_plot_l8) <- c("DATE", "Method", "Temperature")
ggplot(data = Winters_temps_to_plot_l8, aes(DATE, Temperature, colour = Method)) +
geom_line(lwd = 1.3) + ylab("Temperature (°C)") + xlab("Date")
Chapter 09 Some useful tools in R
- Based on the Winters_hours_gaps data set, use magrittr pipes and
functions of the tidyverse to accomplish the following:
- Convert the data set into a tibble
- Select only the top 10 rows of the data set
- Convert the tibble to a long format, with separate rows for Temp_gaps and Temp
- Use ggplot2 to plot Temp_gaps and temp as facets (point or line plot)
- Convert the data set back to the wide format
- Select only the following columns: Year, Month, Day and Temp
- Sort the data set by the Temp column, in descending order
# Convert the data set into a tibble
hourtemps <- Winters_hours_gaps
hourtemps %>% as_tibble() %>% summary() ## Year Month Day Hour
## Min. :2008 Min. : 3.000 Min. : 1.00 Min. : 0.0
## 1st Qu.:2008 1st Qu.: 5.000 1st Qu.: 8.00 1st Qu.: 6.0
## Median :2008 Median : 7.000 Median :15.00 Median :11.0
## Mean :2008 Mean : 6.722 Mean :15.53 Mean :11.5
## 3rd Qu.:2008 3rd Qu.: 9.000 3rd Qu.:23.00 3rd Qu.:17.0
## Max. :2008 Max. :11.000 Max. :31.00 Max. :23.0
##
## Temp_gaps Temp
## Min. : 1.967 Min. : 1.967
## 1st Qu.:13.016 1st Qu.:13.257
## Median :17.915 Median :18.010
## Mean :18.419 Mean :18.644
## 3rd Qu.:23.569 3rd Qu.:23.857
## Max. :38.170 Max. :38.365
## NA's :3374# Select only the top 10 rows of the data set
hourtemps_l9 <- as_tibble(hourtemps[1:10, ])
# Convert the tibble to a long format, with separate rows for Temp_gaps and Temp
hourtemps_long_l9 <- hourtemps_l9 %>% pivot_longer(cols = Temp:Temp_gaps)
# Use ggplot2 to plot Temp_gaps and temp as facets (point or line plot)
ggplot(hourtemps_l9, aes(Hour, Temp_gaps)) + geom_point()
# Convert the data set back to the wide format
hourtemps_wide_l9 <- hourtemps_long_l9 %>% pivot_wider(names_from = name)
# Select only the following columns: Year, Month, Day and Temp
hourtemps_wide_l9 %>% select(c(Month, Day, Temp))## # A tibble: 10 × 3
## Month Day Temp
## <int> <int> <dbl>
## 1 3 3 15.1
## 2 3 3 17.2
## 3 3 3 18.7
## 4 3 3 18.7
## 5 3 3 18.8
## 6 3 3 19.5
## 7 3 3 19.3
## 8 3 3 17.7
## 9 3 3 15.4
## 10 3 3 12.7# Sort the data set by the Temp column, in descending order
hourtemps_wide_l9 %>% arrange(desc(Temp))## # A tibble: 10 × 6
## Year Month Day Hour Temp Temp_gaps
## <int> <int> <int> <int> <dbl> <dbl>
## 1 2008 3 3 15 19.5 19.5
## 2 2008 3 3 16 19.3 19.3
## 3 2008 3 3 14 18.8 18.8
## 4 2008 3 3 12 18.7 18.7
## 5 2008 3 3 13 18.7 18.7
## 6 2008 3 3 17 17.7 17.7
## 7 2008 3 3 11 17.2 17.2
## 8 2008 3 3 18 15.4 15.4
## 9 2008 3 3 10 15.1 15.1
## 10 2008 3 3 19 12.7 12.7- For the Winter_hours_gaps data set, write a for loop to convert all temperatures (Temp column) to degrees Fahrenheit.
HT_l9.2 <- Winters_hours_gaps
HT_l9.2$Temp_F <- NA
for (i in 1:nrow(HT_l9.2))
{HT_l9.2$Temp_F[i] <- (HT_l9.2$Temp[i] * 9/5) + 32}
head(HT_l9.2)## Year Month Day Hour Temp_gaps Temp Temp_F
## 1 2008 3 3 10 15.127 15.127 59.2286
## 2 2008 3 3 11 17.153 17.153 62.8754
## 3 2008 3 3 12 18.699 18.699 65.6582
## 4 2008 3 3 13 18.699 18.699 65.6582
## 5 2008 3 3 14 18.842 18.842 65.9156
## 6 2008 3 3 15 19.508 19.508 67.1144- Execute the same operation with a function from the apply family.
HT_l9.3 <- hourtemps
func_Far_l9.3 <- function(x)
{(x * 9/5) + 32}
head(HT_l9.3)## Year Month Day Hour Temp_gaps Temp
## 1 2008 3 3 10 15.127 15.127
## 2 2008 3 3 11 17.153 17.153
## 3 2008 3 3 12 18.699 18.699
## 4 2008 3 3 13 18.699 18.699
## 5 2008 3 3 14 18.842 18.842
## 6 2008 3 3 15 19.508 19.508for (i in 1:nrow(hourtemps))
{HT_l9.3$Temp_F[i] <- sapply(HT_l9.3$Temp[i], func_Far_l9.3)}
head(HT_l9.3)## Year Month Day Hour Temp_gaps Temp Temp_F
## 1 2008 3 3 10 15.127 15.127 59.2286
## 2 2008 3 3 11 17.153 17.153 62.8754
## 3 2008 3 3 12 18.699 18.699 65.6582
## 4 2008 3 3 13 18.699 18.699 65.6582
## 5 2008 3 3 14 18.842 18.842 65.9156
## 6 2008 3 3 15 19.508 19.508 67.1144- Now use the tidyverse function mutate to achieve the same outcome
HT_l9.2 <- hourtemps %>% mutate(Temp_F = (Temp * 9/5) + 32)
head(HT_l9.2)## Year Month Day Hour Temp_gaps Temp Temp_F
## 1 2008 3 3 10 15.127 15.127 59.2286
## 2 2008 3 3 11 17.153 17.153 62.8754
## 3 2008 3 3 12 18.699 18.699 65.6582
## 4 2008 3 3 13 18.699 18.699 65.6582
## 5 2008 3 3 14 18.842 18.842 65.9156
## 6 2008 3 3 15 19.508 19.508 67.1144Chapter 10 Getting temperature data
- Choose a location of interest and find the 25 closest weather stations using the handle_gsod function.
- location of interest: Dairy farm with own fodder cultivation (belongs to my uncle) Bislich, 46487 Wesel
- coordinates: latitude: 51°40’58.4”N longitude: 6°29’46.4”E
station_list <- handle_gsod(action = "list_stations",
location = c(6.29, 51.40),
time_interval = c(1990, 2020))
station_list <- slice(station_list, 1:25)
head(station_list)## # A tibble: 6 × 10
## chillR_code STATION.NAME CTRY Lat Long BEGIN END Distance
## <chr> <chr> <chr> <dbl> <dbl> <int> <int> <dbl>
## 1 06391099999 ARCEN AWS "NL" 51.5 6.2 19940901 20241127 12.8
## 2 10403099999 MOENCHENGLADBACH "GM" 51.2 6.5 19381001 19421031 23.6
## 3 10437499999 MONCHENGLADBACH "GM" 51.2 6.50 19960715 20241127 24.1
## 4 10405099999 LAARBRUCH (RAF) "GM" 51.6 6.15 19730102 19971231 24.3
## 5 10000199999 NIEDERRHEIN "GM" 51.6 6.14 20050101 20241127 24.7
## 6 99860099999 ARRC MOENCHENGLADBA "" 51.2 6.21 20011015 20020306 24.7
## # ℹ 2 more variables: Overlap_years <dbl>, Perc_interval_covered <dbl>- Download weather data for the most promissing station on the list.
weather_WES <- handle_gsod(action = "download_weather",
location = station_list$chillR_code[1],
time_interval = c(1990, 2020))- Convert the weather data into chillR format and save the data on your PC.
cleaned_weather_WES <- handle_gsod(weather_WES)
cleaned_weather_WES[[1]][1000:1010,]write.csv(station_list, "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/station_list_Wesel.csv", row.names = FALSE)
write.csv(weather_WES[[1]], "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/Wesel_raw_weather.csv", row.names = FALSE)
write.csv(cleaned_weather_WES[[1]], "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/Wesel_chillR_weather.csv", row.names = FALSE)Chapter 11 Filling gaps in temperature records
Before the answers of the exercises for this chapter start, there will be dates added to the dataset because the first 4 years are missing for a reason. So the get added here manually to answer all following exercises correctly.
cleaned_weather_WES <- read.csv("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/Wesel_chillR_weather.csv")
start_date <- as.Date("1990-01-01")
end_date <- as.Date("1993-12-31")
date_sequence <- seq.Date(start_date, end_date, by = "day")
new_rows <- data.frame(
Date = as.POSIXct(paste(date_sequence, "12:00:00")),
Year = as.numeric(format(date_sequence, "%Y")),
Month = as.numeric(format(date_sequence, "%m")),
Day = as.numeric(format(date_sequence, "%d")),
Tmin = NA,
Tmax = NA,
Tmean = NA,
Prec = NA
)
cleaned_weather_WES <- rbind(new_rows, cleaned_weather_WES)- Use chillR functions to find out how many gaps you have in this dataset (even if you have none, please still follow all further steps).
Wesel_QC <- fix_weather(cleaned_weather_WES)$QC
table(is.na(cleaned_weather_WES$Tmin))##
## FALSE TRUE
## 9022 2301Wesel_QC## Season End_year Season_days Data_days Missing_Tmin Missing_Tmax
## 1 1989/1990 1990 365 365 365 365
## 2 1990/1991 1991 365 365 365 365
## 3 1991/1992 1992 366 366 366 366
## 4 1992/1993 1993 365 365 365 365
## 5 1993/1994 1994 365 365 270 270
## 6 1994/1995 1995 365 365 161 161
## 7 1995/1996 1996 366 366 128 128
## 8 1996/1997 1997 365 365 152 152
## 9 1997/1998 1998 365 365 46 46
## 10 1998/1999 1999 365 365 6 6
## 11 1999/2000 2000 366 366 0 0
## 12 2000/2001 2001 365 365 31 31
## 13 2001/2002 2002 365 365 4 4
## 14 2002/2003 2003 365 365 2 2
## 15 2003/2004 2004 366 366 2 2
## 16 2004/2005 2005 365 365 11 11
## 17 2005/2006 2006 365 365 2 2
## 18 2006/2007 2007 365 365 4 4
## 19 2007/2008 2008 366 366 5 5
## 20 2008/2009 2009 365 365 0 0
## 21 2009/2010 2010 365 365 0 0
## 22 2010/2011 2011 365 365 0 0
## 23 2011/2012 2012 366 366 4 4
## 24 2012/2013 2013 365 365 5 5
## 25 2013/2014 2014 365 365 2 2
## 26 2014/2015 2015 365 365 2 2
## 27 2015/2016 2016 366 366 0 0
## 28 2016/2017 2017 365 365 0 0
## 29 2017/2018 2018 365 365 2 2
## 30 2018/2019 2019 365 365 0 0
## 31 2019/2020 2020 366 366 1 1
## Incomplete_days Perc_complete
## 1 365 0.0
## 2 365 0.0
## 3 366 0.0
## 4 365 0.0
## 5 270 26.0
## 6 161 55.9
## 7 128 65.0
## 8 152 58.4
## 9 46 87.4
## 10 6 98.4
## 11 0 100.0
## 12 31 91.5
## 13 4 98.9
## 14 2 99.5
## 15 2 99.5
## 16 11 97.0
## 17 2 99.5
## 18 4 98.9
## 19 5 98.6
## 20 0 100.0
## 21 0 100.0
## 22 0 100.0
## 23 4 98.9
## 24 5 98.6
## 25 2 99.5
## 26 2 99.5
## 27 0 100.0
## 28 0 100.0
## 29 2 99.5
## 30 0 100.0
## 31 1 99.7- Create a list of the 25 closest weather stations using the handle_gsod function.
station_list <- handle_gsod(action = "list_stations",
location = c(6.29, 51.40),
time_interval = c(1990, 2020))
station_list <- slice(station_list, 1:25)
station_list## # A tibble: 25 × 10
## chillR_code STATION.NAME CTRY Lat Long BEGIN END Distance
## <chr> <chr> <chr> <dbl> <dbl> <int> <int> <dbl>
## 1 06391099999 ARCEN AWS "NL" 51.5 6.2 19940901 20241127 12.8
## 2 10403099999 MOENCHENGLADBACH "GM" 51.2 6.5 19381001 19421031 23.6
## 3 10437499999 MONCHENGLADBACH "GM" 51.2 6.50 19960715 20241127 24.1
## 4 10405099999 LAARBRUCH (RAF) "GM" 51.6 6.15 19730102 19971231 24.3
## 5 10000199999 NIEDERRHEIN "GM" 51.6 6.14 20050101 20241127 24.7
## 6 99860099999 ARRC MOENCHENGLADBA "" 51.2 6.21 20011015 20020306 24.7
## 7 10401099999 BRUGGEN (RAF) "GM" 51.2 6.13 19730102 19971231 24.8
## 8 06385099999 DE PEEL(NAFB) "NL" 51.6 5.93 19730402 19940611 29.9
## 9 10402099999 WILDENRATH(GAFB) "GM" 51.1 6.22 19730101 20030508 31.9
## 10 10404399999 KALKAR (MIL COMM) "GM" 51.7 6.17 19820927 19870304 32.6
## # ℹ 15 more rows
## # ℹ 2 more variables: Overlap_years <dbl>, Perc_interval_covered <dbl>- Identify suitable weather stations for patching gaps.
The stations 3, 5, 11 are suitable.
- Download weather data for promising stations, convert them to ChillR format and compile them in a list
patch_weather <- handle_gsod(action = "download_weather",
location = as.character(station_list$chillR_code[c(3, 5, 11)]),
time_interval = c(1990,2020)) %>% handle_gsod()## Loading data for 31 years from station 'NIEDERRHEIN'
## ================================================================================
##
## Loading data for 31 years from station 'DUSSELDORF'
## ================================================================================
##
## Loading data for 31 years from station 'MONCHENGLADBACH'
## ================================================================================- Use the patch_daily_temperatures function to fill gaps
patched <- patch_daily_temperatures(weather = cleaned_weather_WES,
patch_weather = patch_weather,
max_mean_bias = 1,
max_stdev_bias = 2)- Investigate the results - have all gaps been filled?
patched$statistics[[1]]## mean_bias stdev_bias filled gaps_remain
## Tmin -0.703 1.151 36 2265
## Tmax 0.251 0.731 36 2265patched$statistics[[2]]## mean_bias stdev_bias filled gaps_remain
## Tmin -0.541 1.293 1098 1167
## Tmax 0.078 1.036 1098 1167patched$statistics[[3]]## mean_bias stdev_bias filled gaps_remain
## Tmin -1.206 1.705 0 1167
## Tmax 0.220 1.015 256 911- If necessary, repeat until you have a dataset you can work with in further analyses.
station_list## # A tibble: 25 × 10
## chillR_code STATION.NAME CTRY Lat Long BEGIN END Distance
## <chr> <chr> <chr> <dbl> <dbl> <int> <int> <dbl>
## 1 06391099999 ARCEN AWS "NL" 51.5 6.2 19940901 20241127 12.8
## 2 10403099999 MOENCHENGLADBACH "GM" 51.2 6.5 19381001 19421031 23.6
## 3 10437499999 MONCHENGLADBACH "GM" 51.2 6.50 19960715 20241127 24.1
## 4 10405099999 LAARBRUCH (RAF) "GM" 51.6 6.15 19730102 19971231 24.3
## 5 10000199999 NIEDERRHEIN "GM" 51.6 6.14 20050101 20241127 24.7
## 6 99860099999 ARRC MOENCHENGLADBA "" 51.2 6.21 20011015 20020306 24.7
## 7 10401099999 BRUGGEN (RAF) "GM" 51.2 6.13 19730102 19971231 24.8
## 8 06385099999 DE PEEL(NAFB) "NL" 51.6 5.93 19730402 19940611 29.9
## 9 10402099999 WILDENRATH(GAFB) "GM" 51.1 6.22 19730101 20030508 31.9
## 10 10404399999 KALKAR (MIL COMM) "GM" 51.7 6.17 19820927 19870304 32.6
## # ℹ 15 more rows
## # ℹ 2 more variables: Overlap_years <dbl>, Perc_interval_covered <dbl>patch_weather2 <- handle_gsod(action = "download_weather",
location = as.character(station_list$chillR_code[c(3, 4, 5, 7, 9, 11, 12, 13, 15, 16)]),
time_interval = c(1990,2020)) %>%
handle_gsod()## Loading data for 31 years from station 'VOLKEL'
## ================================================================================
##
## Loading data for 31 years from station 'ELL AWS'
## ================================================================================
##
## Loading data for 31 years from station 'NIEDERRHEIN'
## ================================================================================
##
## Loading data for 31 years from station 'DUSSELDORF'
## ================================================================================
##
## Loading data for 31 years from station 'BRUGGEN (RAF)'
## ================================================================================
##
## Loading data for 31 years from station 'WILDENRATH(GAFB)'
## ================================================================================
##
## Loading data for 31 years from station 'KALKAR (MIL COMM)'
## ================================================================================
##
## Loading data for 31 years from station 'LAARBRUCH (RAF)'
## ================================================================================
##
## Loading data for 31 years from station 'ESSEN/MULHEIM'
## ================================================================================
##
## Loading data for 31 years from station 'MONCHENGLADBACH'
## ================================================================================patched_2 <- patch_daily_temperatures(weather = cleaned_weather_WES,
patch_weather = patch_weather2,
max_mean_bias = 1,
max_stdev_bias = 2)
patched_2$statistics[[1]]## mean_bias stdev_bias filled gaps_remain
## Tmin 0.665 1.179 2286 15
## Tmax 0.165 0.873 2286 15patched_2$statistics[[6]]## mean_bias stdev_bias filled gaps_remain
## Tmin 0.459 1.428 0 4
## Tmax -0.109 1.217 0 4patched_2$statistics[[7]]## mean_bias stdev_bias filled gaps_remain
## Tmin -0.302 1.258 3 1
## Tmax -0.018 1.211 3 1post_patch_stats_2 <- fix_weather(patched_2)$QC
post_patch_stats_2## Season End_year Season_days Data_days Missing_Tmin Missing_Tmax
## 1 1989/1990 1990 365 365 0 0
## 2 1990/1991 1991 365 365 0 0
## 3 1991/1992 1992 366 366 0 0
## 4 1992/1993 1993 365 365 0 0
## 5 1993/1994 1994 365 365 0 0
## 6 1994/1995 1995 365 365 0 0
## 7 1995/1996 1996 366 366 0 0
## 8 1996/1997 1997 365 365 0 0
## 9 1997/1998 1998 365 365 0 0
## 10 1998/1999 1999 365 365 1 1
## 11 1999/2000 2000 366 366 0 0
## 12 2000/2001 2001 365 365 0 0
## 13 2001/2002 2002 365 365 0 0
## 14 2002/2003 2003 365 365 0 0
## 15 2003/2004 2004 366 366 0 0
## 16 2004/2005 2005 365 365 0 0
## 17 2005/2006 2006 365 365 0 0
## 18 2006/2007 2007 365 365 0 0
## 19 2007/2008 2008 366 366 0 0
## 20 2008/2009 2009 365 365 0 0
## 21 2009/2010 2010 365 365 0 0
## 22 2010/2011 2011 365 365 0 0
## 23 2011/2012 2012 366 366 0 0
## 24 2012/2013 2013 365 365 0 0
## 25 2013/2014 2014 365 365 0 0
## 26 2014/2015 2015 365 365 0 0
## 27 2015/2016 2016 366 366 0 0
## 28 2016/2017 2017 365 365 0 0
## 29 2017/2018 2018 365 365 0 0
## 30 2018/2019 2019 365 365 0 0
## 31 2019/2020 2020 366 366 0 0
## Incomplete_days Perc_complete
## 1 0 100.0
## 2 0 100.0
## 3 0 100.0
## 4 0 100.0
## 5 0 100.0
## 6 0 100.0
## 7 0 100.0
## 8 0 100.0
## 9 0 100.0
## 10 1 99.7
## 11 0 100.0
## 12 0 100.0
## 13 0 100.0
## 14 0 100.0
## 15 0 100.0
## 16 0 100.0
## 17 0 100.0
## 18 0 100.0
## 19 0 100.0
## 20 0 100.0
## 21 0 100.0
## 22 0 100.0
## 23 0 100.0
## 24 0 100.0
## 25 0 100.0
## 26 0 100.0
## 27 0 100.0
## 28 0 100.0
## 29 0 100.0
## 30 0 100.0
## 31 0 100.0patched_complete <- patched_2$weather %>% make_all_day_table()
Tmin_int <- interpolate_gaps(patched_complete[,"Tmin"])
patched_complete <- patched_complete %>% mutate(Tmin = Tmin_int$interp,
Tmin_interpolated = Tmin_int$missing)
Tmax_int <- interpolate_gaps(patched_complete[,"Tmax"])
patched_complete <- patched_complete %>% mutate(Tmax = Tmax_int$interp,
Tmax_interpolated = Tmax_int$missing)
fix_weather(patched_complete)$QC## Season End_year Season_days Data_days Missing_Tmin Missing_Tmax
## 1 1989/1990 1990 365 365 0 0
## 2 1990/1991 1991 365 365 0 0
## 3 1991/1992 1992 366 366 0 0
## 4 1992/1993 1993 365 365 0 0
## 5 1993/1994 1994 365 365 0 0
## 6 1994/1995 1995 365 365 0 0
## 7 1995/1996 1996 366 366 0 0
## 8 1996/1997 1997 365 365 0 0
## 9 1997/1998 1998 365 365 0 0
## 10 1998/1999 1999 365 365 0 0
## 11 1999/2000 2000 366 366 0 0
## 12 2000/2001 2001 365 365 0 0
## 13 2001/2002 2002 365 365 0 0
## 14 2002/2003 2003 365 365 0 0
## 15 2003/2004 2004 366 366 0 0
## 16 2004/2005 2005 365 365 0 0
## 17 2005/2006 2006 365 365 0 0
## 18 2006/2007 2007 365 365 0 0
## 19 2007/2008 2008 366 366 0 0
## 20 2008/2009 2009 365 365 0 0
## 21 2009/2010 2010 365 365 0 0
## 22 2010/2011 2011 365 365 0 0
## 23 2011/2012 2012 366 366 0 0
## 24 2012/2013 2013 365 365 0 0
## 25 2013/2014 2014 365 365 0 0
## 26 2014/2015 2015 365 365 0 0
## 27 2015/2016 2016 366 366 0 0
## 28 2016/2017 2017 365 365 0 0
## 29 2017/2018 2018 365 365 0 0
## 30 2018/2019 2019 365 365 0 0
## 31 2019/2020 2020 366 366 0 0
## Incomplete_days Perc_complete
## 1 0 100
## 2 0 100
## 3 0 100
## 4 0 100
## 5 0 100
## 6 0 100
## 7 0 100
## 8 0 100
## 9 0 100
## 10 0 100
## 11 0 100
## 12 0 100
## 13 0 100
## 14 0 100
## 15 0 100
## 16 0 100
## 17 0 100
## 18 0 100
## 19 0 100
## 20 0 100
## 21 0 100
## 22 0 100
## 23 0 100
## 24 0 100
## 25 0 100
## 26 0 100
## 27 0 100
## 28 0 100
## 29 0 100
## 30 0 100
## 31 0 100fix_QC <- fix_weather(patched_complete)$QC
fix_QC## Season End_year Season_days Data_days Missing_Tmin Missing_Tmax
## 1 1989/1990 1990 365 365 0 0
## 2 1990/1991 1991 365 365 0 0
## 3 1991/1992 1992 366 366 0 0
## 4 1992/1993 1993 365 365 0 0
## 5 1993/1994 1994 365 365 0 0
## 6 1994/1995 1995 365 365 0 0
## 7 1995/1996 1996 366 366 0 0
## 8 1996/1997 1997 365 365 0 0
## 9 1997/1998 1998 365 365 0 0
## 10 1998/1999 1999 365 365 0 0
## 11 1999/2000 2000 366 366 0 0
## 12 2000/2001 2001 365 365 0 0
## 13 2001/2002 2002 365 365 0 0
## 14 2002/2003 2003 365 365 0 0
## 15 2003/2004 2004 366 366 0 0
## 16 2004/2005 2005 365 365 0 0
## 17 2005/2006 2006 365 365 0 0
## 18 2006/2007 2007 365 365 0 0
## 19 2007/2008 2008 366 366 0 0
## 20 2008/2009 2009 365 365 0 0
## 21 2009/2010 2010 365 365 0 0
## 22 2010/2011 2011 365 365 0 0
## 23 2011/2012 2012 366 366 0 0
## 24 2012/2013 2013 365 365 0 0
## 25 2013/2014 2014 365 365 0 0
## 26 2014/2015 2015 365 365 0 0
## 27 2015/2016 2016 366 366 0 0
## 28 2016/2017 2017 365 365 0 0
## 29 2017/2018 2018 365 365 0 0
## 30 2018/2019 2019 365 365 0 0
## 31 2019/2020 2020 366 366 0 0
## Incomplete_days Perc_complete
## 1 0 100
## 2 0 100
## 3 0 100
## 4 0 100
## 5 0 100
## 6 0 100
## 7 0 100
## 8 0 100
## 9 0 100
## 10 0 100
## 11 0 100
## 12 0 100
## 13 0 100
## 14 0 100
## 15 0 100
## 16 0 100
## 17 0 100
## 18 0 100
## 19 0 100
## 20 0 100
## 21 0 100
## 22 0 100
## 23 0 100
## 24 0 100
## 25 0 100
## 26 0 100
## 27 0 100
## 28 0 100
## 29 0 100
## 30 0 100
## 31 0 100table(is.na(patched_complete$Tmin))##
## FALSE
## 11323write.csv(patched_complete, "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/patched_weather_3_Wesel.csv", row.names = FALSE)Chapter 12 Generating temperature scenarios
- For the location you chose for your earlier analyses, use chillR’s weather generator to produce 100 years of synthetic temperature data.
patched_complete <- read.csv("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/patched_weather_3_Wesel.csv")
# we simulate here with the data form 1998 to 2009 100 times how the weather could have been in this time
Temp <- patched_complete %>%
temperature_generation(years = c(1998,2009),
sim_years = c(2001,2100))## Warning: scenario doesn't contain named elements - consider using the following
## element names: 'data', 'reference_year','scenario_type','labels'## Warning: setting scenario_type to 'relative'## Warning: Reference year missing - can't check if relative temperature scenario
## is validTemperatures <- patched_complete %>% filter(Year %in% 1998:2009) %>%
cbind(Data_source = "observed") # cbind() adds more coloumns to the table
new_data <- Temp[[1]] %>%
select(Year, Month, Day, Tmin, Tmax) %>% # Nur die gewünschten Spalten auswählen
mutate(Data_source = "simulated") # Neue Spalte Data_source hinzufügen
# Daten an Temperatures anhängen
Temperatures <- bind_rows(Temperatures, new_data)
Temperatures <- Temperatures %>%
mutate(Date = as.Date(ISOdate(2000, Month, Day))) # mutate creates a coloumn from already existing dataggplot(data = Temperatures,
aes(Date,
Tmin)) +
geom_smooth(aes(colour = factor(Year))) +
facet_wrap(vars(Data_source)) +
theme_bw(base_size = 20) +
theme(legend.position = "none") +
scale_x_date(date_labels = "%b")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(data = Temperatures,
aes(Date,
Tmax)) + # creates a empty coordinate system
geom_smooth(aes(colour = factor(Year))) + # creates a smoothing regression to make the data better obsevable (alternative is geom_line() then you getalso the nice colors but without the smoothing evect)
facet_wrap(vars(Data_source)) + #
theme_bw(base_size = 20) +
theme(legend.position = "none") + # deletes the legend (is here only confusing)
scale_x_date(date_labels = "%b") # shows on the xachsis only the month without the year## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
- Calculate winter chill (in Chill Portions) for your synthetic weather, and illustrate your results as histograms and cumulative distributions.
chill_observed <- Temperatures %>%
filter(Data_source == "observed") %>%
stack_hourly_temps(latitude = 50.4) %>%
chilling(Start_JDay = 305,
End_JDay = 59)
chill_simulated <- Temperatures %>%
filter(Data_source == "simulated") %>%
stack_hourly_temps(latitude = 51.4) %>%
chilling(Start_JDay = 305,
End_JDay = 59)
chill_comparison <-
cbind(chill_observed,
Data_source = "observed") %>%
rbind(cbind(chill_simulated,
Data_source = "simulated"))
chill_comparison_full_seasons <-
chill_comparison %>%
filter(Perc_complete == 100)
chill_comparison_full_seasons$Data_source <- factor(
chill_comparison_full_seasons$Data_source,
levels = c("simulated", "observed")
)
chill_comparison_full_seasons$Data_source <- factor(
chill_comparison_full_seasons$Data_source,
levels = c("simulated", "observed")
)
chill_comparison_full_seasons <- chill_comparison_full_seasons[order(
chill_comparison_full_seasons$Data_source), ]ggplot(chill_comparison_full_seasons,
aes(x = Chill_portions)) +
geom_histogram(binwidth = 1, position = "identity",
aes(fill = factor(Data_source))) +
scale_fill_brewer(palette="Set2") +
theme_bw(base_size = 20) +
labs(fill = "Data source") +
xlab("Chill accumulation (Chill Portions)") +
ylab("Frequency")
chill_simulations <-
chill_comparison_full_seasons %>%
filter(Data_source == "simulated")
ggplot(chill_simulations,
aes(x = Chill_portions)) +
stat_ecdf(geom = "step",
lwd = 1.5,
col = "blue") +
ylab("Cumulative probability") +
xlab("Chill accumulation (in Chill Portions)") +
theme_bw(base_size = 20)
- Produce similar plots for the number of freezing hours (<0°C) in April (or October, if your site is in the Southern Hemisphere) for your location of interest.
# filter only our observed data and calculate with the coordinates (latitude) the chilling day
chill_observed <- Temperatures %>%
filter(Data_source == "observed") %>%
stack_hourly_temps(latitude = 51.4) %>%
chilling(Start_JDay = 305,
End_JDay = 59)
### adding a freezing model - to make this for April, you'll also have to adjust the dates in the calculations
df <- data.frame(
lower= c(-1000, 0),
upper= c( 0, 1000),
weight=c( 1, 0))
freezing_hours <- function(x) step_model(x,df)
freezing_hours(c(1, 2, 4, 5, -10))## [1] 0 0 0 0 1chill_observed <- Temperatures %>%
filter(Data_source == "observed") %>%
stack_hourly_temps(latitude = 51.4) %>%
tempResponse(Start_JDay = 305, # more flexible than the chilling() function, we feed it our freezing function
End_JDay = 59,
models = list(Frost = freezing_hours,
Chill_portions = Dynamic_Model,
GDH = GDH))
####
# repeating the freezing function with the simulated data
chill_simulated <- Temperatures %>%
filter(Data_source == "simulated") %>%
stack_hourly_temps(latitude = 51.4) %>%
tempResponse(Start_JDay = 305,
End_JDay = 59,
models=list(Frost = freezing_hours,
Chill_portions = Dynamic_Model,
GDH = GDH))
# combine the freezing data for the observed and the simulated
chill_comparison <-
cbind(chill_observed,
Data_source = "observed") %>%
rbind(cbind(chill_simulated,
Data_source = "simulated"))
# sometimes there is data missing for the winter and the influence the data negative, so we remove them
chill_comparison_full_seasons <-
chill_comparison %>%
filter(Perc_complete == 100)
chill_comparison_full_seasons$Data_source <- factor(
chill_comparison_full_seasons$Data_source,
levels = c("simulated", "observed")
)
chill_comparison_full_seasons <- chill_comparison_full_seasons[order(
chill_comparison_full_seasons$Data_source), ]# plot the data as a histogram
ggplot(chill_comparison_full_seasons,
aes(x = Frost)) +
geom_histogram(binwidth = 25, position = "identity",
aes(fill = factor(Data_source))) +
scale_fill_brewer(palette="Set2") +
theme_bw(base_size = 10) +
labs(fill = "Data source") +
xlab("Frost incidence during winter (hours)") +
ylab("Frequency")
chill_simulations <-
chill_comparison_full_seasons %>%
filter(Data_source == "simulated")
# shows how many frost hours are how likely to happen
ggplot(chill_simulations,
aes(x = Frost)) +
stat_ecdf(geom = "step", # accumulated density function
lwd = 1.5,
col = "blue") +
ylab("Cumulative probability") +
xlab("Frost incidence during winter (hours)") +
theme_bw(base_size = 20)
# Here's the amount of chill that is exceeded in 90% of all years.
quantile(chill_simulations$Chill_portions, 0.1)## 10%
## 78.80063# and here's the 50% confidence interval (25th to 75th percentile)
quantile(chill_simulations$Chill_portions, c(0.25, 0.75))## 25% 75%
## 82.04394 86.97673Chapter 13 Saving and loading data (and hiding this in markdown)
There are no tasks for this chapter.
Chapter 14 Historic temperature scenarios
- For the location you chose for previous exercises, produce historic temperature scenarios representing several years of the historic record (your choice).
patched_complete <- read.csv("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/patched_weather_3_Wesel.csv")
Temp <- patched_complete %>%
temperature_generation(years = c(1998,2005),
sim_years = c(2001,2100))
change_scenario <- data.frame(Tmin = rep(2,12),
Tmax = rep(2,12))
# change_scenario
Temp_2 <- temperature_generation(KA_weather,
years = c(1998,2005),
sim_years = c(2001,2100),
temperature_scenario = change_scenario)
Temperature_scenarios <- KA_weather %>%
filter(Year %in% 1998:2005) %>%
cbind(Data_source = "observed") %>%
rbind(Temp[[1]] %>%
select(c(Year, Month, Day, Tmin, Tmax)) %>%
cbind(Data_source = "simulated")
) %>%
rbind(Temp_2[[1]] %>%
select(c(Year, Month, Day, Tmin, Tmax)) %>%
cbind(Data_source = "Warming_2C")
) %>%
mutate(Date = as.Date(ISOdate(2000,
Month,
Day)))
ggplot(data = Temperature_scenarios,
aes(Date,Tmin)) +
geom_smooth(aes(colour = factor(Year))) +
facet_wrap(vars(Data_source)) +
theme_bw(base_size = 20) +
theme(legend.position = "none") +
scale_x_date(date_labels = "%b")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(data = Temperature_scenarios,
aes(Date,Tmax)) +
geom_smooth(aes(colour = factor(Year))) +
facet_wrap(vars(Data_source)) +
theme_bw(base_size = 20) +
theme(legend.position = "none") +
scale_x_date(date_labels = "%b")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
summary(patched_complete)## YEARMODA DATE Date Year
## Min. :19900101 Length:11323 Length:11323 Min. :1990
## 1st Qu.:19971002 Class :character Class :character 1st Qu.:1997
## Median :20050702 Mode :character Mode :character Median :2005
## Mean :20050675 Mean :2005
## 3rd Qu.:20130402 3rd Qu.:2013
## Max. :20201231 Max. :2020
##
## Month Day Tmin Tmax
## Min. : 1.000 Min. : 1.00 Min. :-19.889 Min. :-10.278
## 1st Qu.: 4.000 1st Qu.: 8.00 1st Qu.: 2.276 1st Qu.: 9.222
## Median : 7.000 Median :16.00 Median : 6.722 Median : 15.111
## Mean : 6.523 Mean :15.73 Mean : 6.484 Mean : 15.112
## 3rd Qu.:10.000 3rd Qu.:23.00 3rd Qu.: 11.111 3rd Qu.: 21.000
## Max. :12.000 Max. :31.00 Max. : 23.278 Max. : 40.222
##
## Tmean Prec Tmin_source Tmax_source
## Min. :-15.222 Min. : 0.000 Length:11323 Length:11323
## 1st Qu.: 5.944 1st Qu.: 0.000 Class :character Class :character
## Median : 10.916 Median : 0.000 Mode :character Mode :character
## Mean : 10.784 Mean : 1.952
## 3rd Qu.: 15.944 3rd Qu.: 2.032
## Max. : 31.278 Max. :78.486
## NA's :2301 NA's :2301
## Tmin_interpolated Tmax_interpolated
## Mode :logical Mode :logical
## FALSE:11322 FALSE:11322
## TRUE :1 TRUE :1
##
##
##
## scenario_1990 <- temperature_scenario_from_records(weather = patched_complete,
year = 1990)
temps_1990 <- temperature_generation(weather = patched_complete,
years = c(1990,2020),
sim_years = c(2001,2100),
temperature_scenario = scenario_1990)
scenario_2005 <- temperature_scenario_from_records(weather = patched_complete,
year = 2005)
relative_scenario <- temperature_scenario_baseline_adjustment(
baseline = scenario_2005,
temperature_scenario = scenario_1990)
temps_1990<-temperature_generation(weather = patched_complete,
years = c(1990,2020),
sim_years = c(2001,2100),
temperature_scenario = relative_scenario)
all_past_scenarios <- temperature_scenario_from_records(
weather = patched_complete,
year = c(1990,
1997,
2003,
2010))
adjusted_scenarios <- temperature_scenario_baseline_adjustment(
baseline = scenario_2005,
temperature_scenario = all_past_scenarios)
all_past_scenario_temps <- temperature_generation(
weather = patched_complete,
years = c(1990,2020),
sim_years = c(2001,2100),
temperature_scenario = adjusted_scenarios)
save_temperature_scenarios(all_past_scenario_temps, "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters", "Wesel_hist_scenarios")- Produce chill distributions for these scenarios and plot them.
frost_model <- function(x)
step_model(x,
data.frame(
lower=c(-1000,0),
upper=c(0,1000),
weight=c(1,0)))
models <- list(Chill_Portions = Dynamic_Model,
GDH = GDH,
Frost_H = frost_model)
chill_hist_scenario_list <- tempResponse_daily_list(all_past_scenario_temps,
latitude = 51.4,
Start_JDay = 305,
End_JDay = 59,
models = models)
chill_hist_scenario_list <- lapply(chill_hist_scenario_list,
function(x) x %>%
filter(Perc_complete == 100))
save_temperature_scenarios(chill_hist_scenario_list, "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters", "Wesel_hist_chill_305_59")scenarios <- names(chill_hist_scenario_list)[1:4]
all_scenarios <- chill_hist_scenario_list[[scenarios[1]]] %>%
mutate(scenario = as.numeric(scenarios[1]))
for (sc in scenarios[2:4])
all_scenarios <- all_scenarios %>%
rbind(chill_hist_scenario_list[[sc]] %>%
cbind(
scenario=as.numeric(sc))
) %>%
filter(Perc_complete == 100)
# Let's compute the actual 'observed' chill for comparison
actual_chill <- tempResponse_daily_list(patched_complete,
latitude=51.4,
Start_JDay = 305,
End_JDay = 59,
models)[[1]] %>%
filter(Perc_complete == 100)
ggplot(data = all_scenarios,
aes(scenario,
Chill_Portions,
fill = factor(scenario))) +
geom_violin() +
ylab("Chill accumulation (Chill Portions)") +
xlab("Scenario year") +
theme_bw(base_size = 15) +
ylim(c(0,90)) +
geom_point(data = actual_chill,
aes(End_year,
Chill_Portions,
fill = "blue"),
col = "blue",
show.legend = FALSE) +
scale_fill_discrete(name = "Scenario",
breaks = unique(all_scenarios$scenario))## Warning: Removed 10 rows containing non-finite outside the scale range
## (`stat_ydensity()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
write.csv(actual_chill,"D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/Wesel_observed_chill_305_59.csv", row.names = FALSE)temperature_means <-
data.frame(Year = min(patched_complete$Year):max(patched_complete$Year),
Tmin = aggregate(patched_complete$Tmin,
FUN = "mean",
by = list(patched_complete$Year))[,2],
Tmax=aggregate(patched_complete$Tmax,
FUN = "mean",
by = list(patched_complete$Year))[,2]) %>%
mutate(runn_mean_Tmin = runn_mean(Tmin,15),
runn_mean_Tmax = runn_mean(Tmax,15))
Tmin_regression <- lm(Tmin~Year,
temperature_means)
Tmax_regression <- lm(Tmax~Year,
temperature_means)
temperature_means <- temperature_means %>%
mutate(regression_Tmin = Tmin_regression$coefficients[1]+
Tmin_regression$coefficients[2]*temperature_means$Year,
regression_Tmax = Tmax_regression$coefficients[1]+
Tmax_regression$coefficients[2]*temperature_means$Year
)
ggplot(temperature_means,
aes(Year,
Tmin)) +
geom_point() +
geom_line(data = temperature_means,
aes(Year,
runn_mean_Tmin),
lwd = 2,
col = "blue") +
geom_line(data = temperature_means,
aes(Year,
regression_Tmin),
lwd = 2,
col = "red") +
theme_bw(base_size = 15) +
ylab("Mean monthly minimum temperature (°C)")
ggplot(temperature_means,
aes(Year,
Tmax)) +
geom_point() +
geom_line(data = temperature_means,
aes(Year,
runn_mean_Tmax),
lwd = 2,
col = "blue") +
geom_line(data = temperature_means,
aes(Year,
regression_Tmax),
lwd = 2,
col = "red") +
theme_bw(base_size = 15) +
ylab("Mean monthly maximum temperature (°C)")
Chapter 15 Future temperature scenarios
- Briefly describe the differences between the RCPs and the SSPs (you may have to follow some of the links provided above).
The main differences between RCPs (Representative Concentration Pathways) ans SSPs (Shared Socioeconomic Pathways) are the following:
Focus
RCPs are defined through greenhouse gas concentrations and radioactive forcing levels
SSPs include socioeconomic factors (e.g. population growth, energy use, policy decisions) along with emissions pathways
Drivers of Change
RCPs only consider atmospheric greenhouse gas concentrations and their effects on climate
SSPs include also social, economic and technological factors that influence emissions
Policy Relevance
RCPs provide no direct link to human actions or policy decisions
SSPs offer insights into how different societal choices impact climate change
Emissions Pathway Representation
RCPs directly define future greenhouse gas concentration levels
SSPs can be paired with radioactive forcing levels for more flexibility
RCPs are considered now as outdated and SSPs are now mainly used
Chapter 16 Making CMIP6 scenarios
- Analyse the historic and future impact of climate change on two agroclimatic metrics of your choice, for the location you’ve chosen for your earlier analyses.
location = c(6.29, 51.40)
area <- c(52.5, 5, 50.5, 7)
download_cmip6_ecmwfr(
scenarios = c("ssp126", "ssp245", "ssp370", "ssp585"),
area = area,
key = '9d522e37-4b90-46d1-8701-7deadd45032b',
model = 'default',
frequency = 'monthly',
variable = c('Tmin', 'Tmax'),
year_start = 2015,
year_end = 2100)download_baseline_cmip6_ecmwfr(
area = area,
key = '9d522e37-4b90-46d1-8701-7deadd45032b',
model = 'match_downloaded',
frequency = 'monthly',
variable = c('Tmin', 'Tmax'),
year_start = 1986,
year_end = 2014,
month = 1:12)library(ncdf4)
library(PCICt)
Wesel_temps <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/patched_weather_3_Wesel.csv")
station <- data.frame(
station_name = c("ARCEN AWS"),
longitude = c(6.5),
latitude = c(51.8))
extracted <- extract_cmip6_data(stations = station)## Unzipping files## Extracting downloaded CMIP6 files# head(extracted$`ssp126_AWI-CM-1-1-MR`)
change_scenarios <-
gen_rel_change_scenario(extracted,
scenarios = c(2050, 2085),
reference_period = c(1986:2014),
future_window_width = 30)
head(change_scenarios)## # A tibble: 6 × 11
## location Month Tmax Tmin scenario start_year end_year scenario_year
## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 ARCEN AWS 1 1.90 1.95 ssp126 2035 2065 2050
## 2 ARCEN AWS 2 2.60 2.00 ssp126 2035 2065 2050
## 3 ARCEN AWS 3 1.40 1.04 ssp126 2035 2065 2050
## 4 ARCEN AWS 4 1.38 1.15 ssp126 2035 2065 2050
## 5 ARCEN AWS 5 1.87 1.56 ssp126 2035 2065 2050
## 6 ARCEN AWS 6 1.86 1.67 ssp126 2035 2065 2050
## # ℹ 3 more variables: reference_year <int>, scenario_type <chr>, labels <chr>write.csv(change_scenarios, "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/all_change_scenarios_ssp126.csv", row.names = FALSE)change_scenarios <- read.csv("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/all_change_scenarios_ssp126.csv")
scen_list <- convert_scen_information(change_scenarios)
scen_frame <- convert_scen_information(scen_list)
# scen_list$"ARCEN AWS"$ssp126$`ACCESS-CM2`$'2050'temps_2005 <- temperature_scenario_from_records(Wesel_temps,
2005)
temps_2000 <- temperature_scenario_from_records(Wesel_temps,
2000)
# temps_2005
# temps_2000
# Wesel_temps
base <- temperature_scenario_baseline_adjustment(temps_2005,
temps_2000)
scen_list <- convert_scen_information(change_scenarios,
give_structure = FALSE)
adjusted_list <- temperature_scenario_baseline_adjustment(base,
scen_list,
temperature_check_args =
list(scenario_check_thresholds = c(-5, 15)))temps <- temperature_generation(Wesel_temps,
years = c(1990, 2020),
sim_years = c(2001, 2100),
adjusted_list,
temperature_check_args =
list( scenario_check_thresholds = c(-5, 15)))
save_temperature_scenarios(temps, "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate", "Wesel_future")for(scen in 1:length(adjusted_list))
{
if(!file.exists(paste0("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate/Wesel_future_",
scen,"_",
names(adjusted_list)[scen],".csv")) )
{temp_temp <- temperature_generation(Wesel_temps,
years = c(1990, 2020),
sim_years = c(2001, 2100),
adjusted_list[scen],
temperature_check_args =
list( scenario_check_thresholds = c(-5, 15)))
write.csv(temp_temp[[1]],paste0("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate/Wesel_future_",scen,"_",names(adjusted_list)[scen],".csv"),
row.names=FALSE)
print(paste("Processed object",scen,"of", length(adjusted_list)))
}
}temps <- load_temperature_scenarios("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate", "Wesel_future_")
frost_model <- function(x)
step_model(x,
data.frame(
lower = c(-1000, 0),
upper = c(0, 1000),
weight = c(1, 0)))
models <- list(Chill_Portions = Dynamic_Model,
GDH = GDH,
Frost_H = frost_model)chill_future_scenario_list <-
tempResponse_daily_list(temps,
latitude = 51.4,
Start_JDay = 305,
End_JDay = 59,
models = models)
chill_future_scenario_list <-
lapply(chill_future_scenario_list,
function(x) x %>%
filter(Perc_complete == 100))
save_temperature_scenarios(chill_future_scenario_list,
"D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate",
"Wesel_futurechill")
chill_future_scenario_list_frost <-
lapply(chill_future_scenario_list_frost,
function(x) x %>%
filter(Perc_complete == 100))
save_temperature_scenarios(chill_future_scenario_list_frost,
"D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate",
"Wesel_futurefrost")Wesel_temps <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/patched_weather_3_Wesel.csv")
observed_chill <- tempResponse_daily_list(Wesel_temps,
latitude = 51.4,
Start_JDay = 305,
End_JDay = 59,
models = models)
observed_chill <- lapply(observed_chill,
function(x) x %>%
filter(Perc_complete == 100))
write.csv(observed_chill, "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/Wesel_observed_chill_305_59.csv")hist_temps <- load_temperature_scenarios("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters", "Wesel_hist_chill_")
chill_hist_scenario_list <- tempResponse_daily_list(hist_temps,
latitude = 51.4,
Start_JDay = 305,
End_JDay = 59,
models = models)
chill_hist_scenario_list <- lapply(chill_hist_scenario_list,
function(x) x %>%
filter(Perc_complete == 100))
save_temperature_scenarios(chill_hist_scenario_list,
"D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate",
"Wesel_hist_chill_305_59")chill_future_scenario_list <- load_temperature_scenarios("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate", "Wesel_futurechill")
chill_hist_scenario_list<-load_temperature_scenarios("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters", "Wesel_hist_chill_305_59")
observed_chill <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/Wesel_observed_chill_305_59.csv")
chills <- make_climate_scenario(
metric_summary = chill_hist_scenario_list,
caption = "Historical",
historic_data = observed_chill,
time_series = TRUE)
plot_climate_scenarios(
climate_scenario_list = chills,
metric = "Chill_Portions",
metric_label = "Chill (Chill Portions)")
## [[1]]
## [1] "time series labels"list_ssp <-
strsplit(names(chill_future_scenario_list),
'\\.') %>%
map(2) %>%
unlist()
list_gcm <-
strsplit(names(chill_future_scenario_list), '\\.') %>%
map(3) %>%
unlist()
list_time <-
strsplit(names(chill_future_scenario_list), '\\.') %>%
map(4) %>%
unlist()
SSPs <- c("ssp126", "ssp245", "ssp370", "ssp585")
Times <- c(2050, 2085)
for(SSP in SSPs)
for(Time in Times)
{
# find all scenarios for the ssp and time
chill <- chill_future_scenario_list[list_ssp == SSP &
list_time == Time]
names(chill) <- list_gcm[list_ssp == SSP &
list_time == Time]
if(SSP == "ssp126") SSPcaption <- "SSP1"
if(SSP == "ssp245") SSPcaption <- "SSP2"
if(SSP == "ssp370") SSPcaption <- "SSP3"
if(SSP == "ssp585") SSPcaption <- "SSP5"
chills <- chill %>%
make_climate_scenario(
caption = c(SSPcaption,
Time),
add_to = chills)
}
info_chill <-
plot_climate_scenarios(
climate_scenario_list = chills,
metric = "Chill_Portions",
metric_label = "Chill (Chill Portions)",
texcex = 1)
info_heat <-
plot_climate_scenarios(
climate_scenario_list = chills,
metric = "GDH",
metric_label = "Heat (Growing Degree Hours)",
texcex = 1)
info_frost <-
plot_climate_scenarios(
climate_scenario_list=chills,
metric="Frost_H",
metric_label="Frost incidence (hours)",
texcex=1)
info_chill[[2]]## code Label
## 1 1 INM-CM4-8
## 2 2 MPI-ESM1-2-LR
## 3 3 CMCC-ESM2
## 4 4 MIROC-ES2L
## 5 5 FIO-ESM-2-0
## 6 6 CNRM-CM6-1-HR
## 7 7 INM-CM5-0
## 8 8 MRI-ESM2-0
## 9 9 EC-Earth3-Veg-LR
## 10 10 GFDL-ESM4
## 11 11 MIROC6
## 12 12 CNRM-ESM2-1
## 13 13 IPSL-CM6A-LR
## 14 14 NESM3
## 15 15 FGOALS-g3
## 16 16 ACCESS-CM2
## 17 17 AWI-CM-1-1-MR
## 18 18 CanESM5Chapter 17 Making CMIP5 scenarios with the ClimateWizard
There are no tasks for this chapter.
Chapter 18 Plotting future scenarios
- Produce similar plots for the weather station you selected for earlier exercises.
chill_hist_scenario_list <- load_temperature_scenarios("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters", "Wesel_hist_chill_305_59")
actual_chill <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/Wesel_observed_chill_305_59.csv")
chill_future_scenario_list <- load_temperature_scenarios("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate", "Wesel_futurechill")
chills <- make_climate_scenario(
chill_hist_scenario_list,
caption = "Historic",
historic_data = actual_chill,
time_series = TRUE)
SSPs <- c("ssp126", "ssp245", "ssp370", "ssp585")
Times <- c(2050, 2085)
list_ssp <-
strsplit(names(chill_future_scenario_list), '\\.') %>%
map(2) %>%
unlist()
list_gcm <-
strsplit(names(chill_future_scenario_list), '\\.') %>%
map(3) %>%
unlist()
list_time <-
strsplit(names(chill_future_scenario_list), '\\.') %>%
map(4) %>%
unlist()
for(SSP in SSPs)
for(Time in Times)
{
# find all scenarios for the ssp and time
chill <- chill_future_scenario_list[list_ssp == SSP & list_time == Time]
names(chill) <- list_gcm[list_ssp == SSP & list_time == Time]
if(SSP == "ssp126") SSPcaption <- "SSP1"
if(SSP == "ssp245") SSPcaption <- "SSP2"
if(SSP == "ssp370") SSPcaption <- "SSP3"
if(SSP == "ssp585") SSPcaption <- "SSP5"
if(Time == "2050") Time_caption <- "2050"
if(Time == "2085") Time_caption <- "2085"
chills <- chill %>%
make_climate_scenario(
caption = c(SSPcaption,
Time_caption),
add_to = chills)
}
plot_climate_scenarios(
climate_scenario_list = chills,
metric = "Chill_Portions",
metric_label = "Chill (Chill Portions)",
texcex = 1)
## [[1]]
## [1] "time series labels"
##
## [[2]]
## code Label
## 1 1 INM-CM4-8
## 2 2 MPI-ESM1-2-LR
## 3 3 CMCC-ESM2
## 4 4 MIROC-ES2L
## 5 5 FIO-ESM-2-0
## 6 6 CNRM-CM6-1-HR
## 7 7 INM-CM5-0
## 8 8 MRI-ESM2-0
## 9 9 EC-Earth3-Veg-LR
## 10 10 GFDL-ESM4
## 11 11 MIROC6
## 12 12 CNRM-ESM2-1
## 13 13 IPSL-CM6A-LR
## 14 14 NESM3
## 15 15 FGOALS-g3
## 16 16 ACCESS-CM2
## 17 17 AWI-CM-1-1-MR
## 18 18 CanESM5
##
## [[3]]
## code Label
## 1 1 ACCESS-CM2
## 2 2 CMCC-ESM2
## 3 3 FIO-ESM-2-0
## 4 4 CNRM-CM6-1-HR
## 5 5 AWI-CM-1-1-MR
## 6 6 EC-Earth3-Veg-LR
## 7 7 GFDL-ESM4
## 8 8 CNRM-ESM2-1
## 9 9 CanESM5
## 10 10 FGOALS-g3
## 11 11 INM-CM4-8
## 12 12 INM-CM5-0
## 13 13 IPSL-CM6A-LR
## 14 14 MIROC-ES2L
## 15 15 MIROC6
## 16 16 MPI-ESM1-2-LR
## 17 17 MRI-ESM2-0
## 18 18 NESM3
##
## [[4]]
## code Label
## 1 1 FGOALS-g3
## 2 2 INM-CM4-8
## 3 3 MPI-ESM1-2-LR
## 4 4 CMCC-ESM2
## 5 5 EC-Earth3-CC
## 6 6 MIROC-ES2L
## 7 7 FIO-ESM-2-0
## 8 8 CNRM-CM6-1-HR
## 9 9 INM-CM5-0
## 10 10 MRI-ESM2-0
## 11 11 EC-Earth3-Veg-LR
## 12 12 GFDL-ESM4
## 13 13 MIROC6
## 14 14 CNRM-ESM2-1
## 15 15 IPSL-CM6A-LR
## 16 16 NESM3
## 17 17 ACCESS-CM2
## 18 18 AWI-CM-1-1-MR
##
## [[5]]
## code Label
## 1 1 FGOALS-g3
## 2 2 ACCESS-CM2
## 3 3 CMCC-ESM2
## 4 4 EC-Earth3-CC
## 5 5 FIO-ESM-2-0
## 6 6 CNRM-CM6-1-HR
## 7 7 AWI-CM-1-1-MR
## 8 8 EC-Earth3-Veg-LR
## 9 9 GFDL-ESM4
## 10 10 CNRM-ESM2-1
## 11 11 INM-CM4-8
## 12 12 INM-CM5-0
## 13 13 IPSL-CM6A-LR
## 14 14 MIROC-ES2L
## 15 15 MIROC6
## 16 16 MPI-ESM1-2-LR
## 17 17 MRI-ESM2-0
## 18 18 NESM3
##
## [[6]]
## code Label
## 1 1 CNRM-ESM2-1
## 2 2 IPSL-CM6A-LR
## 3 3 FGOALS-g3
## 4 4 EC-Earth3-AerChem
## 5 5 INM-CM4-8
## 6 6 MPI-ESM1-2-LR
## 7 7 MIROC-ES2L
## 8 8 CNRM-CM6-1-HR
## 9 9 INM-CM5-0
## 10 10 MRI-ESM2-0
## 11 11 EC-Earth3-Veg-LR
## 12 12 GFDL-ESM4
## 13 13 MIROC6
## 14 14 ACCESS-CM2
## 15 15 AWI-CM-1-1-MR
##
## [[7]]
## code Label
## 1 1 CNRM-ESM2-1
## 2 2 FGOALS-g3
## 3 3 EC-Earth3-AerChem
## 4 4 ACCESS-CM2
## 5 5 CNRM-CM6-1-HR
## 6 6 AWI-CM-1-1-MR
## 7 7 EC-Earth3-Veg-LR
## 8 8 GFDL-ESM4
## 9 9 INM-CM4-8
## 10 10 INM-CM5-0
## 11 11 IPSL-CM6A-LR
## 12 12 MIROC-ES2L
## 13 13 MIROC6
## 14 14 MPI-ESM1-2-LR
## 15 15 MRI-ESM2-0
##
## [[8]]
## code Label
## 1 1 CIESM
## 2 2 NESM3
## 3 3 CNRM-ESM2-1
## 4 4 IPSL-CM6A-LR
## 5 5 FGOALS-g3
## 6 6 CMCC-ESM2
## 7 7 INM-CM4-8
## 8 8 MPI-ESM1-2-LR
## 9 9 EC-Earth3-CC
## 10 10 FIO-ESM-2-0
## 11 11 MIROC-ES2L
## 12 12 CNRM-CM6-1-HR
## 13 13 INM-CM5-0
## 14 14 MRI-ESM2-0
## 15 15 EC-Earth3-Veg-LR
## 16 16 GFDL-ESM4
## 17 17 MIROC6
## 18 18 ACCESS-CM2
## 19 19 AWI-CM-1-1-MR
##
## [[9]]
## code Label
## 1 1 CIESM
## 2 2 CNRM-ESM2-1
## 3 3 FGOALS-g3
## 4 4 CMCC-ESM2
## 5 5 ACCESS-CM2
## 6 6 EC-Earth3-CC
## 7 7 FIO-ESM-2-0
## 8 8 CNRM-CM6-1-HR
## 9 9 AWI-CM-1-1-MR
## 10 10 EC-Earth3-Veg-LR
## 11 11 GFDL-ESM4
## 12 12 INM-CM4-8
## 13 13 INM-CM5-0
## 14 14 IPSL-CM6A-LR
## 15 15 MIROC-ES2L
## 16 16 MIROC6
## 17 17 MPI-ESM1-2-LR
## 18 18 MRI-ESM2-0
## 19 19 NESM3for(nam in names(chills[[1]]$data))
{
ch <- chills[[1]]$data[[nam]]
ch[,"GCM"] <- "none"
ch[,"SSP"] <- "none"
ch[,"Year"] <- as.numeric(nam)
if(nam == names(chills[[1]]$data)[1])
past_simulated <- ch else
past_simulated <- rbind(past_simulated,
ch)
}
past_simulated["Scenario"] <- "Historical"
head(past_simulated)## Season End_year Season_days Data_days Perc_complete Chill_Portions
## 1 2001/2002 2002 120 120 100 85.93502
## 2 2002/2003 2003 120 120 100 84.11282
## 3 2003/2004 2004 120 120 100 83.33690
## 4 2004/2005 2005 121 121 100 88.70952
## 5 2005/2006 2006 120 120 100 89.60029
## 6 2006/2007 2007 120 120 100 84.94518
## GDH Frost_H GCM SSP Year Scenario
## 1 3996.664 450 none none 1990 Historical
## 2 3344.907 479 none none 1990 Historical
## 3 1889.238 721 none none 1990 Historical
## 4 2464.805 361 none none 1990 Historical
## 5 3750.400 214 none none 1990 Historical
## 6 3091.575 432 none none 1990 Historicalpast_simulated <- past_simulated %>% filter(Perc_complete == 100)
past_observed <- chills[[1]][["historic_data"]]
head(past_observed)## X Season End_year Season_days Data_days Perc_complete Chill_Portions
## 1 1 1990/1991 1991 120 120 100 77.46451
## 2 2 1991/1992 1992 120 120 100 84.12147
## 3 3 1992/1993 1993 121 121 100 85.89337
## 4 4 1993/1994 1994 120 120 100 83.83349
## 5 5 1994/1995 1995 120 120 100 86.00229
## 6 6 1995/1996 1996 120 120 100 67.05746
## GDH Frost_H
## 1 1964.757 839
## 2 2481.648 491
## 3 3143.898 482
## 4 1904.884 591
## 5 5425.112 317
## 6 1612.944 1224for(i in 2:length(chills))
for(nam in names(chills[[i]]$data))
{ch <- chills[[i]]$data[[nam]]
ch[,"GCM"] <- nam
ch[,"SSP"] <- chills[[i]]$caption[1]
ch[,"Year"] <- chills[[i]]$caption[2]
if(i == 2 & nam == names(chills[[i]]$data)[1])
future_data <- ch else
future_data <- rbind(future_data,ch)
}
head(future_data)## Season End_year Season_days Data_days Perc_complete Chill_Portions
## 1 2001/2002 2002 120 120 100 86.99319
## 2 2002/2003 2003 120 120 100 86.87197
## 3 2003/2004 2004 120 120 100 85.62412
## 4 2004/2005 2005 121 121 100 90.50165
## 5 2005/2006 2006 120 120 100 90.64776
## 6 2006/2007 2007 120 120 100 86.32296
## GDH Frost_H GCM SSP Year
## 1 5318.501 352 INM-CM4-8 SSP1 2050
## 2 4400.557 409 INM-CM4-8 SSP1 2050
## 3 2744.363 578 INM-CM4-8 SSP1 2050
## 4 3436.639 242 INM-CM4-8 SSP1 2050
## 5 5051.034 111 INM-CM4-8 SSP1 2050
## 6 4095.937 350 INM-CM4-8 SSP1 2050metric <- "GDH"
axis_label <- "Heat (in GDH)"
rng <- range(past_observed[[metric]],
past_simulated[[metric]],
future_data[[metric]])
rng## [1] 1088.303 19014.867past_plot <- ggplot() +
geom_boxplot(data = past_simulated,
aes_string("as.numeric(Year)",
metric,
group = "Year"),
fill = "skyblue")## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.past_plot <- past_plot +
scale_y_continuous(
limits = c(0,
round(rng[2] + rng[2]/10))) +
labs(x = "Year",
y = axis_label)
past_plot <- past_plot +
facet_grid(~ Scenario) +
theme_bw(base_size = 15)
past_plot <- past_plot +
theme(strip.background = element_blank(),
strip.text = element_text(face = "bold"),
axis.text.x = element_text(angle = 45,
hjust = 1))
# add historic data
past_plot <- past_plot +
geom_point(data = past_observed,
aes_string("End_year",
metric),
col = "blue")
past_plot
y <- 2050
future_2050 <-
ggplot(data = future_data[future_data$Year == y,]) +
geom_boxplot(aes_string("GCM",
metric,
fill = "GCM"))
future_2050 <- future_2050 +
facet_wrap(vars(SSP), nrow = 1) +
scale_x_discrete(labels = NULL,
expand = expansion(add = 1))
library(ggpmisc)
future_2050 <- future_2050 +
scale_y_continuous(limits = c(0,
round(round(1.1*rng[2])))) +
geom_text_npc(aes(npcx = "center",
npcy = "top",
label = Year),
size = 5)
future_2050 <- future_2050 +
theme_bw(base_size = 15) +
theme(axis.ticks.y = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
legend.position = "bottom",
legend.margin = margin(0,
0,
0,
0,
"cm"),
legend.background = element_rect(),
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
legend.box.spacing = unit(0, "cm"),
plot.subtitle = element_text(hjust = 0.5,
vjust = -1,
size = 15 * 1.05,
face = "bold"))
future_2050
future_plot_list <- list()
time_points <- c(2050, 2085)
for(y in time_points)
{
future_plot_list[[which(y == time_points)]] <-
ggplot(data = future_data[which(future_data$Year==y),]) +
geom_boxplot(aes_string("GCM",
metric,
fill="GCM")) +
facet_wrap(vars(SSP), nrow = 1) +
scale_x_discrete(labels = NULL,
expand = expansion(add = 1)) +
scale_y_continuous(limits = c(0,
round(round(1.1*rng[2])))) +
geom_text_npc(aes(npcx = "center",
npcy = "top",
label = Year),
size = 5) +
theme_bw(base_size = 15) +
theme(axis.ticks.y = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
legend.position = "bottom",
legend.margin = margin(0,
0,
0,
0,
"cm"),
legend.background = element_rect(),
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
legend.box.spacing = unit(0, "cm"),
plot.subtitle = element_text(
hjust = 0.5,
vjust = -1,
size = 15 * 1.05,
face = "bold"))
}
library(patchwork)
both_plots <- past_plot + future_plot_list
plot <- both_plots +
plot_layout(guides = "collect",
widths = c(1,
rep(1.8,
length(future_plot_list))))
plot <- plot & theme(legend.position = "bottom",
legend.text = element_text(size = 8),
legend.title = element_text(size = 10),
axis.title.x = element_blank())
plot
metric <- "Chill_Portions"
axis_label <- "Chill (in CP)"
rng <- range(past_observed[[metric]],
past_simulated[[metric]],
future_data[[metric]])
past_plot <- ggplot() +
geom_boxplot(data = past_simulated,
aes_string("as.numeric(Year)",
metric,group="Year"),
fill="skyblue") +
scale_y_continuous(limits = c(0,
round(round(1.1*rng[2])))) +
labs(x = "Year", y = axis_label) +
facet_grid(~ Scenario) +
theme_bw(base_size = 15) +
theme(strip.background = element_blank(),
strip.text = element_text(face = "bold"),
axis.text.x = element_text(angle=45, hjust=1)) +
geom_point(data = past_observed,
aes_string("End_year",
metric),
col="blue")
future_plot_list <- list()
for(y in c(2050,
2085))
{
future_plot_list[[which(y == c(2050,2085))]] <-
ggplot(data = future_data[which(future_data$Year==y),]) +
geom_boxplot(aes_string("GCM",
metric,
fill="GCM")) +
facet_wrap(vars(SSP), nrow = 1) +
scale_x_discrete(labels = NULL,
expand = expansion(add = 1)) +
scale_y_continuous(limits = c(0,
round(round(1.1*rng[2])))) +
geom_text_npc(aes(npcx = "center",
npcy = "top",
label = Year),
size = 5) +
theme_bw(base_size = 15) +
theme(axis.ticks.y = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
legend.position = "bottom",
legend.margin = margin(0,
0,
0,
0,
"cm"),
legend.background = element_rect(),
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
legend.box.spacing = unit(0, "cm"),
plot.subtitle = element_text(hjust = 0.5,
vjust = -1,
size = 15 * 1.05,
face = "bold"))
}
plot <- (past_plot +
future_plot_list +
plot_layout(guides = "collect",
widths = c(1,rep(1.8,length(future_plot_list))))
) & theme(legend.position = "bottom",
legend.text = element_text(size = 8),
legend.title = element_text(size = 10),
axis.title.x=element_blank())
plot
metric <- "Frost_H"
axis_label <- "Frost duration (in hours)"
# get extreme values for the axis scale
rng <- range(past_observed[[metric]],
past_simulated[[metric]],
future_data[[metric]])
past_plot <- ggplot() +
geom_boxplot(data = past_simulated,
aes_string("as.numeric(Year)",
metric,group="Year"),
fill="skyblue") +
scale_y_continuous(limits = c(0,
round(round(1.1*rng[2])))) +
labs(x = "Year", y = axis_label) +
facet_grid(~ Scenario) +
theme_bw(base_size = 15) +
theme(strip.background = element_blank(),
strip.text = element_text(face = "bold"),
axis.text.x = element_text(angle=45, hjust=1)) +
geom_point(data = past_observed,
aes_string("End_year",
metric),
col="blue")
future_plot_list <- list()
for(y in c(2050,
2085))
{
future_plot_list[[which(y == c(2050,2085))]] <-
ggplot(data = future_data[which(future_data$Year==y),]) +
geom_boxplot(aes_string("GCM",
metric,
fill="GCM")) +
facet_wrap(vars(SSP), nrow = 1) +
scale_x_discrete(labels = NULL,
expand = expansion(add = 1)) +
scale_y_continuous(limits = c(0,
round(round(1.1*rng[2])))) +
geom_text_npc(aes(npcx = "center",
npcy = "top",
label = Year),
size = 5) +
theme_bw(base_size = 15) +
theme(axis.ticks.y = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
legend.position = "bottom",
legend.margin = margin(0,
0,
0,
0,
"cm"),
legend.background = element_rect(),
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
legend.box.spacing = unit(0, "cm"),
plot.subtitle = element_text(hjust = 0.5,
vjust = -1,
size = 15 * 1.05,
face = "bold"))
}
plot <- (past_plot +
future_plot_list +
plot_layout(guides = "collect",
widths = c(1,rep(1.8,length(future_plot_list))))
) & theme(legend.position = "bottom",
legend.text = element_text(size = 8),
legend.title = element_text(size = 10),
axis.title.x=element_blank())
plot
plot_scenarios_gg <- function(past_observed,
past_simulated,
future_data,
metric,
axis_label,
time_points)
{
rng <- range(past_observed[[metric]],
past_simulated[[metric]],
future_data[[metric]])
past_plot <- ggplot() +
geom_boxplot(data = past_simulated,
aes_string("as.numeric(Year)",
metric,
group="Year"),
fill="skyblue") +
scale_y_continuous(limits = c(0,
round(round(1.1*rng[2])))) +
labs(x = "Year", y = axis_label) +
facet_grid(~ Scenario) +
theme_bw(base_size = 15) +
theme(strip.background = element_blank(),
strip.text = element_text(face = "bold"),
axis.text.x = element_text(angle=45,
hjust=1)) +
geom_point(data = past_observed,
aes_string("End_year",
metric),
col="blue")
future_plot_list <- list()
for(y in time_points)
{
future_plot_list[[which(y == time_points)]] <-
ggplot(data = future_data[which(future_data$Year==y),]) +
geom_boxplot(aes_string("GCM",
metric,
fill="GCM")) +
facet_wrap(vars(SSP), nrow = 1) +
scale_x_discrete(labels = NULL,
expand = expansion(add = 1)) +
scale_y_continuous(limits = c(0,
round(round(1.1*rng[2])))) +
geom_text_npc(aes(npcx = "center",
npcy = "top",
label = Year),
size = 5) +
theme_bw(base_size = 15) +
theme(axis.ticks.y = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
legend.position = "bottom",
legend.margin = margin(0,
0,
0,
0,
"cm"),
legend.background = element_rect(),
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
legend.box.spacing = unit(0, "cm"),
plot.subtitle = element_text(hjust = 0.5,
vjust = -1,
size = 15 * 1.05,
face = "bold"))
}
plot <- (past_plot +
future_plot_list +
plot_layout(guides = "collect",
widths = c(1,rep(1.8,length(future_plot_list))))
) & theme(legend.position = "bottom",
legend.text = element_text(size=8),
legend.title = element_text(size=10),
axis.title.x=element_blank())
plot
}plot_scenarios_gg(past_observed = past_observed,
past_simulated = past_simulated,
future_data = future_data,
metric = "GDH",
axis_label = "Heat (in Growing Degree Hours)",
time_points = c(2050, 2085))
ggsave("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/Wesel_heat_plot.png",
width = 10,
height = 8,
dpi = 600)
plot_scenarios_gg(past_observed = past_observed,
past_simulated = past_simulated,
future_data = future_data,
metric = "Chill_Portions",
axis_label = "Chill (in Chill Portions)",
time_points = c(2050, 2085))
ggsave("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/Wesel_Chill_portions_plot.png",
width = 10,
height = 8,
dpi = 600)
plot_scenarios_gg(past_observed = past_observed,
past_simulated = past_simulated,
future_data = future_data,
metric = "Frost_H",
axis_label = "Frost duration (in hours)",
time_points = c(2050, 2085))
ggsave("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/Wesel_frost_plot.png",
width = 10,
height = 8,
dpi = 600)Chapter 19 Chill model comparison
- Perform a similar analysis for the location you’ve chosen for your exercises.
hourly_models <- list(Chilling_units = chilling_units,
Low_chill = low_chill_model,
Modified_Utah = modified_utah_model,
North_Carolina = north_carolina_model,
Positive_Utah = positive_utah_model,
Chilling_Hours = Chilling_Hours,
Utah_Chill_Units = Utah_Model,
Chill_Portions = Dynamic_Model)
daily_models <- list(Rate_of_Chill = rate_of_chill,
Chill_Days = chill_days,
Exponential_Chill = exponential_chill,
# Triangular_Chill_Haninnen = triangular_chill_1,
Triangular_Chill_Legave = triangular_chill_2)
metrics <- c(names(daily_models),
names(hourly_models))
model_labels = c("Rate of Chill",
"Chill Days",
"Exponential Chill",
# "Triangular Chill (Häninnen)",
"Triangular Chill (Legave)",
"Chilling Units",
"Low-Chill Chill Units",
"Modified Utah Chill Units",
"North Carolina Chill Units",
"Positive Utah Chill Units",
"Chilling Hours",
"Utah Chill Units",
"Chill Portions")
data.frame(Metric = model_labels, 'Function name' = metrics)## Metric Function.name
## 1 Rate of Chill Rate_of_Chill
## 2 Chill Days Chill_Days
## 3 Exponential Chill Exponential_Chill
## 4 Triangular Chill (Legave) Triangular_Chill_Legave
## 5 Chilling Units Chilling_units
## 6 Low-Chill Chill Units Low_chill
## 7 Modified Utah Chill Units Modified_Utah
## 8 North Carolina Chill Units North_Carolina
## 9 Positive Utah Chill Units Positive_Utah
## 10 Chilling Hours Chilling_Hours
## 11 Utah Chill Units Utah_Chill_Units
## 12 Chill Portions Chill_PortionsWesel_temps <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/patched_weather_3_Wesel.csv")
Temps <- load_temperature_scenarios("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters", "Wesel_hist_scenarios")Start_JDay <- 305
End_JDay <- 59
daily_models_past_scenarios <-
tempResponse_list_daily(Temps,
Start_JDay = Start_JDay,
End_JDay = End_JDay,
models=daily_models)
daily_models_past_scenarios <- lapply(
daily_models_past_scenarios,
function(x) x[which(x$Perc_complete>90),])
hourly_models_past_scenarios<-
tempResponse_daily_list(Temps,
latitude = 51.405,
Start_JDay = Start_JDay,
End_JDay = End_JDay,
models = hourly_models,
misstolerance = 10)
past_scenarios <- daily_models_past_scenarios
past_scenarios <- lapply(
names(past_scenarios),
function(x)
cbind(past_scenarios[[x]],
hourly_models_past_scenarios[[x]][,names(hourly_models)]))
names(past_scenarios) <- names(daily_models_past_scenarios)
daily_models_observed <-
tempResponse_daily(Wesel_temps,
Start_JDay = Start_JDay,
End_JDay = End_JDay,
models = daily_models)
daily_models_observed <-
daily_models_observed[which(daily_models_observed$Perc_complete>90),]
hourly_models_observed <-
tempResponse_daily_list(Wesel_temps,
latitude=51.405,
Start_JDay = Start_JDay,
End_JDay = End_JDay,
models = hourly_models,
misstolerance = 10)
past_observed <- cbind(
daily_models_observed,
hourly_models_observed[[1]][,names(hourly_models)])
save_temperature_scenarios(past_scenarios,
"D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate", "Wesel_multichill_305_59_historic")
write.csv(past_observed,
"D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate/Wesel_multichill_305_59_observed.csv",
row.names=FALSE)SSPs <- c("ssp126", "ssp245","ssp370", "ssp585")
Times <- c(2050, 2085)
future_temps <- load_temperature_scenarios("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate","Wesel_future_")
list_ssp <-
strsplit(names(future_temps), '\\.') %>%
map(2) %>%
unlist()
list_gcm <-
strsplit(names(future_temps), '\\.') %>%
map(3) %>%
unlist()
list_time <-
strsplit(names(future_temps), '\\.') %>%
map(4) %>%
unlist()for(SSP in SSPs)
{
for(Time in Times)
{
Temps <- future_temps[list_ssp == SSP & list_time == Time]
names(Temps) <- list_gcm[list_ssp == SSP & list_time == Time]
daily_models_future_scenarios <- tempResponse_list_daily(
Temps,
Start_JDay = Start_JDay,
End_JDay = End_JDay,
models = daily_models)
daily_models_future_scenarios<-lapply(
daily_models_future_scenarios,
function(x) x[which(x$Perc_complete>90),])
hourly_models_future_scenarios<-
tempResponse_daily_list(
Temps,
latitude = 51.405,
Start_JDay = Start_JDay,
End_JDay = End_JDay,
models=hourly_models,
misstolerance = 10)
future_scenarios <- daily_models_future_scenarios
future_scenarios <- lapply(
names(future_scenarios),
function(x)
cbind(future_scenarios[[x]],
hourly_models_future_scenarios[[x]][,names(hourly_models)]))
names(future_scenarios)<-names(daily_models_future_scenarios)
chill<-future_scenarios
save_temperature_scenarios(
chill,
"D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate",
paste0("Wesel_multichill_305_59_",Time,"_",SSP))
}
}chill_past_scenarios <- load_temperature_scenarios(
"D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate",
"Wesel_multichill_305_59_historic")
chill_observed <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate/Wesel_multichill_305_59_observed.csv")
chills <- make_climate_scenario(chill_past_scenarios,
caption = "Historical",
historic_data = chill_observed,
time_series = TRUE)
for(SSP in SSPs)
for(Time in Times)
{
chill <- load_temperature_scenarios(
"D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/future_climate",
paste0("Wesel_multichill_305_59_",Time,"_",SSP))
if(SSP == "ssp126") SSPcaption <- "SSP1"
if(SSP == "ssp245") SSPcaption <- "SSP2"
if(SSP == "ssp370") SSPcaption <- "SSP3"
if(SSP == "ssp585") SSPcaption <- "SSP5"
if(Time == "2050") Time_caption <- "2050"
if(Time == "2085") Time_caption <- "2085"
chills <- make_climate_scenario(chill,
caption = c(SSPcaption,
Time_caption),
add_to = chills)
}
for(i in 1:length(chills))
{ch <- chills[[i]]
if(ch$caption[1] == "Historical")
{GCMs <- rep("none",length(names(ch$data)))
SSPs <- rep("none",length(names(ch$data)))
Years <- as.numeric(ch$labels)
Scenario <- rep("Historical",
length(names(ch$data)))} else
{GCMs <- names(ch$data)
SSPs <- rep(ch$caption[1],
length(names(ch$data)))
Years <- rep(as.numeric(ch$caption[2]),
length(names(ch$data)))
Scenario <- rep("Future",
length(names(ch$data)))}
for(nam in names(ch$data))
{for(met in metrics)
{temp_res <-
data.frame(Metric = met,
GCM = GCMs[which(nam == names(ch$data))],
SSP = SSPs[which(nam == names(ch$data))],
Year = Years[which(nam == names(ch$data))],
Result = quantile(ch$data[[nam]][,met],0.1),
Scenario = Scenario[which(nam == names(ch$data))])
if(i == 1 & nam == names(ch$data)[1] & met == metrics[1])
results <- temp_res else
results <- rbind(results,
temp_res)
}
}
}
for(met in metrics)
results[which(results$Metric == met),"SWC"] <-
results[which(results$Metric == met),"Result"]/
results[which(results$Metric == met & results$Year == 1990),
"Result"]-1- Make a heat map illustrating past and future changes in Safe Winter Chill, relative to a past scenario, for the 13 chill models used here.
rng = range(results$SWC)
p_future <- ggplot(results[which(!results$GCM == "none"),],
aes(GCM,
y = factor(Metric,
levels = metrics),
fill = SWC)) +
geom_tile()
p_future <-
p_future +
facet_grid(SSP ~ Year)
p_future <-
p_future +
theme_bw(base_size = 15) +
theme(axis.text = element_text(size=6))
library(colorRamps)
p_future <-
p_future +
scale_fill_gradientn(colours = matlab.like(15),
labels = scales::percent,
limits = rng)
p_future <-
p_future +
theme(axis.text.x = element_text(angle = 75,
hjust = 1,
vjust = 1)) +
labs(fill = "Change in\nSafe Winter Chill\nsince 1980") +
scale_y_discrete(labels = model_labels) +
ylab("Chill metric")
p_future
p_past<-
ggplot(results[which(results$GCM == "none"),],
aes(Year,
y = factor(Metric,
levels=metrics),
fill = SWC)) +
geom_tile()
p_past<-
p_past +
theme_bw(base_size = 15) +
theme(axis.text = element_text(size = 6))
p_past<-
p_past +
scale_fill_gradientn(colours = matlab.like(15),
labels = scales::percent,
limits = rng)
p_past<-
p_past +
scale_x_continuous(position = "top")
p_past<-
p_past +
labs(fill = "Change in\nSafe Winter Chill\nsince 1980") +
scale_y_discrete(labels = model_labels) +
ylab("Chill metric")
p_past
chill_comp_plot<-
(p_past +
p_future +
plot_layout(guides = "collect",
nrow = 2,
heights = c(1,3))) &
theme(legend.position = "right",
strip.background = element_blank(),
strip.text = element_text(face = "bold"))
chill_comp_plot
- Produce an animated line plot of your results (summarizing Safe Winter Chill across all the GCMs).
hist_results <- results[which(results$GCM == "none"),]
hist_results$SSP <- "SSP1"
hist_results_2 <- hist_results
hist_results_2$SSP <- "SSP2"
hist_results_3 <- hist_results
hist_results_3$SSP <- "SSP3"
hist_results_4 <- hist_results
hist_results_4$SSP <- "SSP5"
hist_results <- rbind(hist_results,
hist_results_2,
hist_results_3,
hist_results_4)
future_results <- results[which(!results$GCM == "none"),]
GCM_aggregate <- aggregate(
future_results$SWC,
by=list(future_results$Metric,
future_results$SSP,
future_results$Year),
FUN=mean)
colnames(GCM_aggregate) <- c("Metric",
"SSP",
"Year",
"SWC")
SSP_Time_series<-rbind(hist_results[,c("Metric",
"SSP",
"Year",
"SWC")],
GCM_aggregate)
SSP_Time_series$Year <- as.numeric(SSP_Time_series$Year)
chill_change_plot<-
ggplot(data = SSP_Time_series,
aes(x = Year,
y = SWC,
col = factor(Metric,
levels = metrics))) +
geom_line(lwd = 1.3) +
facet_wrap(~SSP,
nrow = 4) +
theme_bw(base_size = 10) +
labs(col = "Change in\nSafe Winter Chill\nsince 1980") +
scale_color_discrete(labels = model_labels) +
scale_y_continuous(labels = scales::percent) +
theme(strip.background = element_blank(),
strip.text = element_text(face = "bold")) +
ylab("Safe Winter Chill")
chill_change_plot
library(gganimate)
library(gifski)## Warning: Paket 'gifski' wurde unter R Version 4.4.2 erstelltlibrary(png)
library(transformr)## Warning: Paket 'transformr' wurde unter R Version 4.4.2 erstelltccp<-chill_change_plot +
transition_reveal(Year)
animate(ccp, fps = 10)## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
anim_save("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/chill_comparison_animation.gif",
animation = last_animation())Chapter 20 Simple phenology analysis
Provide a brief narrative describing what p-hacking is, and why this is a problematic approach to data analysis.
p-hacking
Is a manipulative approach to data analysis aimed at achieving statistically significant results. Common techniques include selective reporting, cherry-picking variables, or running multiple tests until the p-value falls below 0.05.
Problems
It increases false positive results which can create misleading conclusions. It also undermines scientific integrity and makes it impossible or very difficult to reproduce results. Additionally, it encourages to only publish “positive” results. All this leads to wasting resources, it can mislead future research and it lowers the public trust in science.
Provide a sketch of your casual understanding of the relationship between temperature and bloom dates.
Warmer winter and spring temperatures generally lead to earlier bloom dates. Plants require a certain amount of warmth (growing degree days) to sprout in spring. However, they also need a period of cold (chilling requirement) to properly transition into growth.
Some species need a specific number of cold days in winter before they can respond to warmth. After meeting the chilling requirement, plants need sufficient warmth to trigger blooming. If one requirement isn’t met the plant may fail to bloom or show reduced productivity.
What do we need to know to build a process-based model from this?
Key inputs
Temperature data (daily min/max, long-term trends)
Species-specific chilling and heat accumulation thresholds
Day length (photoperiod) for each day
Environmental factors influencing temperature (e.g., topography)
Model Structure
Accumulate chilling degree days from a defined starting point in autumn
After chilling requirements are met, track growing degree days
Link daily min/max temperatures with day length to estimate hourly temperature trends
Chapter 21 Simple phenology analysis
Briefly explain why you shouldn’t take the results of a PLS regression analysis between temperature and phenology at face value. What do you need in addition in order to make sense of such outputs?
PLS is is a useful mining tool, but not a good analytical method on it’s own, especially for small datasets. If there are only limited observations and a high variability, then the results can be misleading when the attempt of an interpretation is made. PLS should therefore always be used with a strong theoretical framework to validate or challenge existing knowledge about e.g. temperature responses.
Temperature data with a high amount of data (like daily or even hourly data) is a challenge, because the phenology data that it is compared with is very limited (e.g. one bloom date or period per year). Creating a statistical correlation between these two is rather difficult. The effects of temperature vary over time, and simple regression approaches often fail to capture these complexities. That is why PLS results should always be cross validated with other models or experimental data to ensure meaningful interpretations.
Replicate the PLS analysis for the Roter Boskoop dataset that you used in a previous lesson.
Boskoop <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/data ch20-25/Roter_Boskoop_bloom_1958_2019.csv") %>%
select(Pheno_year, First_bloom) %>%
mutate(Year = as.numeric(substr(First_bloom, 1, 4)),
Month = as.numeric(substr(First_bloom, 5, 6)),
Day = as.numeric(substr(First_bloom, 7, 8))) %>%
make_JDay() %>%
select(Pheno_year, JDay) %>%
rename(Year = Pheno_year,
pheno = JDay)
# head(Boskoop)
KA_temps <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/data ch20-25/TMaxTMin1958-2019_patched.csv") %>%
make_JDay()
# head(KA_temps)
PLS_results <- PLS_pheno(KA_temps,
Boskoop)
# head(PLS_results$PLS_summary)ggplot_PLS <- function(PLS_results)
{
library(ggplot2)
PLS_gg <- PLS_results$PLS_summary %>%
mutate(Month = trunc(Date / 100),
Day = Date - Month * 100,
Date = NULL)
PLS_gg$Date <- ISOdate(2002,
PLS_gg$Month,
PLS_gg$Day)
PLS_gg$Date[PLS_gg$JDay <= 0] <-
ISOdate(2001,
PLS_gg$Month[PLS_gg$JDay <= 0],
PLS_gg$Day[PLS_gg$JDay <= 0])
PLS_gg <- PLS_gg %>%
mutate(VIP_importance = VIP >= 0.8,
VIP_Coeff = factor(sign(Coef) * VIP_importance))
VIP_plot<- ggplot(PLS_gg,aes(x=Date,y=VIP)) +
geom_bar(stat='identity',aes(fill=VIP>0.8))
VIP_plot <- VIP_plot +
scale_fill_manual(name="VIP",
labels = c("<0.8", ">0.8"),
values = c("FALSE" = "grey",
"TRUE" = "blue")) +
theme_bw(base_size=15) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank())
coeff_plot <- ggplot(PLS_gg,
aes(x = Date,
y = Coef)) +
geom_bar(stat ='identity',
aes(fill = VIP_Coeff)) +
scale_fill_manual(name = "Effect direction",
labels = c("Advancing",
"Unimportant",
"Delaying"),
values = c("-1" = "red",
"0" = "grey",
"1" = "dark green")) +
theme_bw(base_size = 15) +
ylab("PLS coefficient") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank())
temp_plot <- ggplot(PLS_gg) +
geom_ribbon(aes(x = Date,
ymin = Tmean - Tstdev,
ymax = Tmean + Tstdev),
fill = "grey") +
geom_ribbon(aes(x = Date,
ymin = Tmean - Tstdev * (VIP_Coeff == -1),
ymax = Tmean + Tstdev * (VIP_Coeff == -1)),
fill = "red") +
geom_ribbon(aes(x = Date,
ymin = Tmean - Tstdev * (VIP_Coeff == 1),
ymax = Tmean + Tstdev * (VIP_Coeff == 1)),
fill = "dark green") +
geom_line(aes(x = Date,
y = Tmean)) +
theme_bw(base_size = 15) +
ylab(expression(paste(T[mean]," (°C)")))
library(patchwork)
plot<- (VIP_plot +
coeff_plot +
temp_plot +
plot_layout(ncol=1,
guides = "collect")
) & theme(legend.position = "right",
legend.text = element_text(size = 8),
legend.title = element_text(size = 10),
axis.title.x = element_blank())
plot}
ggplot_PLS(PLS_results)
Write down your thoughts on why we’re not seeing the temperature response pattern we may have expected. What happened to the chill response?
The expected chilling response is unclear in the PLS analysis, possibly because of some data limitations, overlapping chilling and forcing phases, or the chosen analysis parameters. Chilling accumulates over an extended period of time, and the model may not fully capture this dynamic. Also the smoothing effect of the 11-day running mean or an incorrect start date for the phenological year might also hide some chilling signals.
Additionally, environmental factors beyond temperature, such as photoperiod or soil moisture, could influence the plants but these are not included as an influence in the model.
Chapter 22 Successes and limitations of PLS regression analysis
Briefly explain in what climatic settings we can expect PLS regression to detect the chilling phase - and in what settings this probably wont work.
PLS would be able to detect the chilling phase in locations with mild winters where warm temperatures reduce the chill accumulation (like in California). In regions with colder winters (like Beijing, China) where a temperature change can increase and decrease chilling it is way more difficult for PLS to detect chilling phases. This is because if it gets too cold the chill accumulation stops and this creates a dynamic that a simple PLS can not detect.
How could we overcome this problem?
We could create a model that explicitly calculates the chill accumulation instead of an RLS or we apply alternative statistical methods that can capture the nonlinear relationship between chilling and the temperature.
Chapter 23 PLS regression with agroclimatic metrics
- Repeat the PLS_chill_force procedure for the ‘Roter Boskoop’ dataset. Include plots of daily chill and heat accumulation.
temps_hourly <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/data ch20-25/TMaxTMin1958-2019_patched.csv") %>%
stack_hourly_temps(latitude = 50.6)
daychill <- daily_chill(hourtemps = temps_hourly,
running_mean = 1,
models = list(
Chilling_Hours = Chilling_Hours,
Utah_Chill_Units = Utah_Model,
Chill_Portions = Dynamic_Model,
GDH = GDH))
dc <- make_daily_chill_plot2(daychill,
metrics = c("Chill_Portions"),
cumulative = FALSE,
startdate = 300,
enddate = 30,
focusyears = c(2008),
metriclabels = "Chill Portions")
dc <- make_daily_chill_plot2(daychill,
metrics = c("Chill_Portions"),
cumulative = TRUE,
startdate = 300,
enddate = 30,
focusyears = c(2008),
metriclabels = "Chill Portions")
Boskoop <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/data ch20-25/Roter_Boskoop_bloom_1958_2019.csv") %>%
select(Pheno_year, First_bloom) %>%
mutate(Year = as.numeric(substr(First_bloom, 1, 4)),
Month = as.numeric(substr(First_bloom, 5, 6)),
Day = as.numeric(substr(First_bloom, 7, 8))) %>%
make_JDay() %>%
select(Pheno_year,
JDay) %>%
rename(Year = Pheno_year,
pheno = JDay)
plscf <- PLS_chill_force(daily_chill_obj = daychill,
bio_data_frame = Boskoop,
split_month = 6,
chill_models = "Chill_Portions",
heat_models = "GDH")plot_PLS(plscf,
PLS_results_path = "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/data ch20-25/plscf_outputs")
plscf <- PLS_chill_force(daily_chill_obj = daychill,
bio_data_frame = Boskoop,
split_month = 6,
chill_models = "Chill_Portions",
heat_models = "GDH",
runn_means = 11)
plot_PLS(plscf,
PLS_results_path = "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/data ch20-25/plscf_outputs_11days")
plot_PLS(plscf,
PLS_results_path = "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/data ch20-25/plscf_outputs_11days_periods",
add_chill = c(-48,62),
add_heat = c(3,105.5))
PLS_gg <- plscf$Chill_Portions$GDH$PLS_summary %>%
mutate(Month = trunc(Date/100),
Day = Date - Month * 100,
Date = ISOdate(2002,
Month,
Day))
PLS_gg[PLS_gg$JDay <= 0,"Date"]<-
ISOdate(2001,
PLS_gg$Month[PLS_gg$JDay <= 0],
PLS_gg$Day[PLS_gg$JDay <= 0])
PLS_gg <- PLS_gg %>%
mutate(VIP_importance = VIP >= 0.8,
VIP_Coeff = factor(sign(Coef) * VIP_importance))
chill_start_JDay <- -48
chill_end_JDay <- 62
heat_start_JDay <- 10
heat_end_JDay <- 116.5
chill_start_date <- ISOdate(2001,
12,
31) + chill_start_JDay * 24 * 3600
chill_end_date <- ISOdate(2001,
12,
31) + chill_end_JDay * 24 * 3600
heat_start_date <- ISOdate(2001,
12,
31) + heat_start_JDay * 24 * 3600
heat_end_date <- ISOdate(2001,
12,
31) + heat_end_JDay * 24 * 3600
temp_plot <- ggplot(PLS_gg,
x = Date) +
annotate("rect",
xmin = chill_start_date,
xmax = chill_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "blue") +
annotate("rect",
xmin = heat_start_date,
xmax = heat_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "red") +
annotate("rect",
xmin = ISOdate(2001,
12,
31) +
min(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
xmax = ISOdate(2001,
12,
31) +
max(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "black") +
geom_vline(xintercept = ISOdate(2001,
12,
31) +
median(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
linetype = "dashed") +
geom_ribbon(aes(x = Date,
ymin = MetricMean - MetricStdev ,
ymax = MetricMean + MetricStdev),
fill="grey") +
geom_ribbon(aes(x = Date,
ymin = MetricMean - MetricStdev * (VIP_Coeff == -1),
ymax = MetricMean + MetricStdev * (VIP_Coeff == -1)),
fill = "red") +
geom_ribbon(aes(x = Date,
ymin = MetricMean - MetricStdev * (VIP_Coeff == 1),
ymax = MetricMean + MetricStdev * (VIP_Coeff == 1)),
fill = "dark green") +
geom_line(aes(x = Date,
y = MetricMean ))
temp_plot
plot_PLS_chill_force <- function(plscf,
chill_metric = "Chill_Portions",
heat_metric = "GDH",
chill_label = "CP",
heat_label = "GDH",
chill_phase = c(-48, 62),
heat_phase = c(10, 116.5))
{
PLS_gg <- plscf[[chill_metric]][[heat_metric]]$PLS_summary %>%
mutate(Month = trunc(Date/100),
Day = Date - Month * 100,
Date = ISOdate(2002,
Month,
Day))
PLS_gg[PLS_gg$JDay <= 0,"Date"]<-
ISOdate(2001,
PLS_gg$Month[PLS_gg$JDay <= 0],
PLS_gg$Day[PLS_gg$JDay <= 0])
PLS_gg <- PLS_gg %>%
mutate(VIP_importance = VIP >= 0.8,
VIP_Coeff = factor(sign(Coef) * VIP_importance))
chill_start_date <- ISOdate(2001,
12,
31) + chill_phase[1] * 24 * 3600
chill_end_date <- ISOdate(2001,
12,
31) + chill_phase[2] * 24 * 3600
heat_start_date <- ISOdate(2001,
12,
31) + heat_phase[1] * 24 * 3600
heat_end_date <- ISOdate(2001,
12,
31) + heat_phase[2] * 24 * 3600
temp_plot <- ggplot(PLS_gg) +
annotate("rect",
xmin = chill_start_date,
xmax = chill_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "blue") +
annotate("rect",
xmin = heat_start_date,
xmax = heat_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "red") +
annotate("rect",
xmin = ISOdate(2001,
12,
31) +
min(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
xmax = ISOdate(2001,
12,
31) +
max(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "black") +
geom_vline(xintercept = ISOdate(2001,
12,
31) +
median(plscf$pheno$pheno,
na.rm=TRUE) * 24 * 3600,
linetype = "dashed") +
geom_ribbon(aes(x = Date,
ymin = MetricMean - MetricStdev ,
ymax = MetricMean + MetricStdev ),
fill = "grey") +
geom_ribbon(aes(x = Date,
ymin = MetricMean - MetricStdev * (VIP_Coeff == -1),
ymax = MetricMean + MetricStdev * (VIP_Coeff == -1)),
fill = "red") +
geom_ribbon(aes(x = Date,
ymin = MetricMean - MetricStdev * (VIP_Coeff == 1),
ymax = MetricMean + MetricStdev * (VIP_Coeff == 1)),
fill = "dark green") +
geom_line(aes(x = Date,
y = MetricMean)) +
facet_wrap(vars(Type),
scales = "free_y",
strip.position = "left",
labeller =
labeller(
Type =
as_labeller(c(Chill = paste0("Chill (",
chill_label,
")"),
Heat = paste0("Heat (",
heat_label,
")"))))) +
ggtitle("Daily chill and heat accumulation rates") +
theme_bw(base_size = 15) +
theme(strip.background = element_blank(),
strip.placement = "outside",
strip.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
axis.title.y = element_blank()
)
VIP_plot <- ggplot(PLS_gg,
aes(x = Date,
y = VIP)) +
annotate("rect",
xmin = chill_start_date,
xmax = chill_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "blue") +
annotate("rect",
xmin = heat_start_date,
xmax = heat_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "red") +
annotate("rect",
xmin = ISOdate(2001,
12,
31) + min(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
xmax = ISOdate(2001,
12,
31) + max(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "black") +
geom_vline(xintercept = ISOdate(2001,
12,
31) + median(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
linetype = "dashed") +
geom_bar(stat = 'identity',
aes(fill = VIP>0.8)) +
facet_wrap(vars(Type),
scales = "free",
strip.position = "left",
labeller =
labeller(
Type = as_labeller(c(Chill="VIP for chill",
Heat="VIP for heat")))) +
scale_y_continuous(
limits = c(0,
max(plscf[[chill_metric]][[heat_metric]]$PLS_summary$VIP))) +
ggtitle("Variable Importance in the Projection (VIP) scores") +
theme_bw(base_size = 15) +
theme(strip.background = element_blank(),
strip.placement = "outside",
strip.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
axis.title.y = element_blank()
) +
scale_fill_manual(name = "VIP",
labels = c("<0.8", ">0.8"),
values = c("FALSE" = "grey",
"TRUE" = "blue")) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
coeff_plot <- ggplot(PLS_gg,
aes(x = Date,
y = Coef)) +
annotate("rect",
xmin = chill_start_date,
xmax = chill_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "blue") +
annotate("rect",
xmin = heat_start_date,
xmax = heat_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "red") +
annotate("rect",
xmin = ISOdate(2001,
12,
31) + min(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
xmax = ISOdate(2001,
12,
31) + max(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "black") +
geom_vline(xintercept = ISOdate(2001,
12,
31) + median(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
linetype = "dashed") +
geom_bar(stat = 'identity',
aes(fill = VIP_Coeff)) +
facet_wrap(vars(Type),
scales = "free",
strip.position = "left",
labeller =
labeller(
Type = as_labeller(c(Chill = "MC for chill",
Heat = "MC for heat")))) +
scale_y_continuous(
limits = c(min(plscf[[chill_metric]][[heat_metric]]$PLS_summary$Coef),
max(plscf[[chill_metric]][[heat_metric]]$PLS_summary$Coef))) +
ggtitle("Model coefficients (MC)") +
theme_bw(base_size = 15) +
theme(strip.background = element_blank(),
strip.placement = "outside",
strip.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
axis.title.y = element_blank()
) +
scale_fill_manual(name = "Effect direction",
labels = c("Advancing",
"Unimportant",
"Delaying"),
values = c("-1" = "red",
"0" = "grey",
"1" = "dark green")) +
ylab("PLS coefficient") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
library(patchwork)
plot <- (VIP_plot +
coeff_plot +
temp_plot +
plot_layout(ncol = 1,
guides = "collect")
) & theme(legend.position = "right",
legend.text = element_text(size = 8),
legend.title = element_text(size = 10),
axis.title.x = element_blank())
plot
}
plot_PLS_chill_force(plscf)
Run PLS_chill_force analyses for all three major chill models. Delineate your best estimates of chilling and forcing phases for all of them.
Plot results for all three analyses, including shaded plot areas for the chilling and forcing periods you estimated.
daychill <- daily_chill(hourtemps = temps_hourly,
running_mean = 11,
models = list(Chilling_Hours = Chilling_Hours,
Utah_Chill_Units = Utah_Model,
Chill_Portions = Dynamic_Model,
GDH = GDH))
plscf <- PLS_chill_force(daily_chill_obj = daychill,
bio_data_frame = Boskoop,
split_month = 6,
chill_models = c("Chilling_Hours",
"Utah_Chill_Units",
"Chill_Portions"),
heat_models = c("GDH"))
plot_PLS_chill_force(plscf,
chill_metric = "Chilling_Hours",
heat_metric = "GDH",
chill_label = "CH",
heat_label = "GDH",
chill_phase = c(-30, 13),
heat_phase = c(-10, 116.5))
plot_PLS_chill_force(plscf,
chill_metric = "Utah_Chill_Units",
heat_metric = "GDH",
chill_label = "CU",
heat_label = "GDH",
chill_phase = c(-70, 20),
heat_phase = c(10, 116.5))
plot_PLS_chill_force(plscf,
chill_metric = "Chill_Portions",
heat_metric = "GDH",
chill_label = "CP",
heat_label = "GDH",
chill_phase = c(-75, 20),
heat_phase = c(0, 116.5))
Chapter 24 Examples of PLS regression with agroclimatic metrics
Look across all the PLS results presented above. Can you detect a pattern in where chilling and forcing periods delineated clearly, and where this attempt failed?
Chilling period
In warm climates like in Croatia or Tunisia is the chilling period is clearly visible.
- The chilling period is only as long as it needs to be or sometimes even too short what makes it a lot easier to detect the chilling period.
In cold climates like in Beijing or even Klein-Altendorf is the chilling period not easy to detect.
- The chilling period is longer than required, what makes it difficult to detect a clear chilling phase
Forcing period
It is easy to detect the forcing period in basically all climates.
This is probably do to the fact that the heat accumulation has a more direct influence on blooming than chilling.
Think about possible reasons for the success or failure of PLS based on agroclimatic metrics. Write down your thoughts.
Reasons for success
Detecting the two phases is easier in climates that have a clearer separation between the chilling and the forcing phase.
If the chilling requirements are just met then the response is clearer to detect.
The forcing period is nearly always good to detect because it has a more direct influence to blooming than chilling.
The choice of Chill model also has an influence on the results, a more simple model like Chilling Hours get less satisfying results than the Dynamic Model.
Reasons for failure
A rather cold climate with a longer chilling period and an overlapping chilling and forcing period is difficult to analyse with a PLS analysis.
PLS analysis assumes linear relationships, which may not capture the complex interactions between chilling, heat, and bloom timing. If only forcing period and blooming are viewed there is a more linear relationship that PLS can detect.
Chapter 25 Why PLS doesn’t always work
- Produce chill and heat model sensitivity plots for the location you focused on in previous exercises.
mon <- 1
ndays <- 31
tmin <- 1
tmax <- 8
latitude <- 51.4
weather <- make_all_day_table(
data.frame(Year = c(2001, 2001),
Month = c(mon, mon),
Day = c(1, ndays),
Tmin = c(0, 0),
Tmax = c(0, 0))) %>%
mutate(Tmin = tmin,
Tmax = tmax)
hourly_temps <- stack_hourly_temps(weather,
latitude = latitude)
CPs <- Dynamic_Model(
hourly_temps$hourtemps$Temp)
daily_CPs <- CPs[length(CPs)] / nrow(weather)
# daily_CPs
month_range <- c(10, 11, 12, 1, 2, 3)
Tmins <- c(-20:20)
Tmaxs <- c(-15:30)
mins <- NA
maxs <- NA
CP <- NA
month <- NA
temp_model <- Dynamic_Model
for(mon in month_range)
{days_month <- as.numeric(
difftime( ISOdate(2002,
mon + 1,
1),
ISOdate(2002,
mon,
1)))
if(mon == 12) days_month <- 31
weather <- make_all_day_table(
data.frame(Year = c(2001, 2001),
Month = c(mon, mon),
Day = c(1, days_month),
Tmin = c(0, 0),
Tmax = c(0, 0)))
for(tmin in Tmins)
for(tmax in Tmaxs)
if(tmax >= tmin)
{
hourtemps <- weather %>%
mutate(Tmin = tmin,
Tmax = tmax) %>%
stack_hourly_temps(latitude = latitude) %>%
pluck("hourtemps", "Temp")
CP <- c(CP,
tail(do.call(temp_model,
list(hourtemps)), 1) /
days_month)
mins <- c(mins, tmin)
maxs <- c(maxs, tmax)
month <- c(month, mon)
}
}
results <- data.frame(Month = month,
Tmin = mins,
Tmax = maxs,
CP)
results <- results[!is.na(results$Month), ]
write.csv(results,
"D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/data ch20-25/model_sensitivity_development.csv",
row.names = FALSE)
results$Month_names <- factor(results$Month,
levels = month_range,
labels = month.name[month_range])
DM_sensitivity <- ggplot(results,
aes(x = Tmin,
y = Tmax,
fill = CP)) +
geom_tile() +
scale_fill_gradientn(colours = alpha(matlab.like(15),
alpha = .5),
name = "Chill/day (CP)") +
ylim(min(results$Tmax),
max(results$Tmax)) +
ylim(min(results$Tmin),
max(results$Tmin))
DM_sensitivity
DM_sensitivity <- DM_sensitivity +
facet_wrap(vars(Month_names)) +
ylim(min(results$Tmax),
max(results$Tmax)) +
ylim(min(results$Tmin),
max(results$Tmin))
DM_sensitivity
latitude <- 51.4
month_range <- c(10, 11, 12, 1, 2, 3)
temperatures <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/patched_weather_3_Wesel.csv") %>%
filter(Month %in% month_range) %>%
mutate(Month_names =
factor(Month,
levels = c(10, 11, 12, 1, 2, 3),
labels = c("October", "November", "December",
"January", "February", "March")))
temperatures[which(temperatures$Tmax < temperatures$Tmin),
c("Tmax", "Tmin")] <- NA
DM_sensitivity +
geom_point(data = temperatures,
aes(x = Tmin,
y = Tmax,
fill = NULL,
color = "Temperature"),
size = 0.2) +
facet_wrap(vars(Month_names)) +
scale_color_manual(values = "black",
labels = "Daily temperature \nextremes (°C)",
name = "Observed at site" ) +
guides(fill = guide_colorbar(order = 1),
color = guide_legend(order = 2)) +
ylab("Tmax (°C)") +
xlab("Tmin (°C)") +
theme_bw(base_size = 15) 
Chapter 26 Evaluating PLS outputs
- Reproduce the analysis for the ‘Roter Boskoop’ dataset.
roter_B <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/data ch20-25/Roter_Boskoop_bloom_1958_2019.csv") %>%
select(Pheno_year, First_bloom) %>%
mutate(Year = as.numeric(substr(First_bloom, 1, 4)),
Month = as.numeric(substr(First_bloom, 5, 6)),
Day = as.numeric(substr(First_bloom, 7, 8))) %>%
make_JDay() %>%
select(Pheno_year,
JDay) %>%
rename(Year = Pheno_year,
pheno = JDay)
temps <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/data ch20-25/TMaxTMin1958-2019_patched.csv")
temps_hourly <- temps %>%
stack_hourly_temps(latitude = 50.6)
daychill <- daily_chill(hourtemps = temps_hourly,
running_mean = 1,
models = list(Chilling_Hours = Chilling_Hours,
Utah_Chill_Units = Utah_Model,
Chill_Portions = Dynamic_Model,
GDH = GDH)
)
plscf <- PLS_chill_force(daily_chill_obj = daychill,
bio_data_frame = roter_B,
split_month = 6,
chill_models = "Chill_Portions",
heat_models = "GDH",
runn_means = 11)plot_PLS_chill_force <- function(plscf,
chill_metric = "Chill_Portions",
heat_metric = "GDH",
chill_label = "CP",
heat_label = "GDH",
chill_phase = c(-48, 62),
heat_phase = c(10, 116.5))
{
PLS_gg <- plscf[[chill_metric]][[heat_metric]]$PLS_summary %>%
mutate(Month = trunc(Date/100),
Day = Date - Month * 100,
Date = ISOdate(2002,
Month,
Day))
PLS_gg[PLS_gg$JDay <= 0,"Date"]<-
ISOdate(2001,
PLS_gg$Month[PLS_gg$JDay <= 0],
PLS_gg$Day[PLS_gg$JDay <= 0])
PLS_gg <- PLS_gg %>%
mutate(VIP_importance = VIP >= 0.8,
VIP_Coeff = factor(sign(Coef) * VIP_importance))
chill_start_date <- ISOdate(2001,
12,
31) + chill_phase[1] * 24 * 3600
chill_end_date <- ISOdate(2001,
12,
31) + chill_phase[2] * 24 * 3600
heat_start_date <- ISOdate(2001,
12,
31) + heat_phase[1] * 24 * 3600
heat_end_date <- ISOdate(2001,
12,
31) + heat_phase[2] * 24 * 3600
temp_plot <- ggplot(PLS_gg) +
annotate("rect",
xmin = chill_start_date,
xmax = chill_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "blue") +
annotate("rect",
xmin = heat_start_date,
xmax = heat_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "red") +
annotate("rect",
xmin = ISOdate(2001,
12,
31) +
min(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
xmax = ISOdate(2001,
12,
31) +
max(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "black") +
geom_vline(xintercept = ISOdate(2001,
12,
31) +
median(plscf$pheno$pheno,
na.rm=TRUE) * 24 * 3600,
linetype = "dashed") +
geom_ribbon(aes(x = Date,
ymin = MetricMean - MetricStdev ,
ymax = MetricMean + MetricStdev ),
fill = "grey") +
geom_ribbon(aes(x = Date,
ymin = MetricMean - MetricStdev * (VIP_Coeff == -1),
ymax = MetricMean + MetricStdev * (VIP_Coeff == -1)),
fill = "red") +
geom_ribbon(aes(x = Date,
ymin = MetricMean - MetricStdev * (VIP_Coeff == 1),
ymax = MetricMean + MetricStdev * (VIP_Coeff == 1)),
fill = "dark green") +
geom_line(aes(x = Date,
y = MetricMean)) +
facet_wrap(vars(Type),
scales = "free_y",
strip.position = "left",
labeller =
labeller(
Type =
as_labeller(c(Chill = paste0("Chill (",
chill_label,
")"),
Heat = paste0("Heat (",
heat_label,
")"))))) +
ggtitle("Daily chill and heat accumulation rates") +
theme_bw(base_size = 15) +
theme(strip.background = element_blank(),
strip.placement = "outside",
strip.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
axis.title.y = element_blank()
)
VIP_plot <- ggplot(PLS_gg,
aes(x = Date,
y = VIP)) +
annotate("rect",
xmin = chill_start_date,
xmax = chill_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "blue") +
annotate("rect",
xmin = heat_start_date,
xmax = heat_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "red") +
annotate("rect",
xmin = ISOdate(2001,
12,
31) + min(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
xmax = ISOdate(2001,
12,
31) + max(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "black") +
geom_vline(xintercept = ISOdate(2001,
12,
31) + median(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
linetype = "dashed") +
geom_bar(stat = 'identity',
aes(fill = VIP>0.8)) +
facet_wrap(vars(Type),
scales = "free",
strip.position = "left",
labeller =
labeller(
Type = as_labeller(c(Chill="VIP for chill",
Heat="VIP for heat")))) +
scale_y_continuous(
limits = c(0,
max(plscf[[chill_metric]][[heat_metric]]$PLS_summary$VIP))) +
ggtitle("Variable Importance in the Projection (VIP) scores") +
theme_bw(base_size = 15) +
theme(strip.background = element_blank(),
strip.placement = "outside",
strip.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
axis.title.y = element_blank()
) +
scale_fill_manual(name = "VIP",
labels = c("<0.8", ">0.8"),
values = c("FALSE" = "grey",
"TRUE" = "blue")) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
coeff_plot <- ggplot(PLS_gg,
aes(x = Date,
y = Coef)) +
annotate("rect",
xmin = chill_start_date,
xmax = chill_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "blue") +
annotate("rect",
xmin = heat_start_date,
xmax = heat_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "red") +
annotate("rect",
xmin = ISOdate(2001,
12,
31) + min(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
xmax = ISOdate(2001,
12,
31) + max(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
ymin = -Inf,
ymax = Inf,
alpha = .1,
fill = "black") +
geom_vline(xintercept = ISOdate(2001,
12,
31) + median(plscf$pheno$pheno,
na.rm = TRUE) * 24 * 3600,
linetype = "dashed") +
geom_bar(stat = 'identity',
aes(fill = VIP_Coeff)) +
facet_wrap(vars(Type),
scales = "free",
strip.position = "left",
labeller =
labeller(
Type = as_labeller(c(Chill = "MC for chill",
Heat = "MC for heat")))) +
scale_y_continuous(
limits = c(min(plscf[[chill_metric]][[heat_metric]]$PLS_summary$Coef),
max(plscf[[chill_metric]][[heat_metric]]$PLS_summary$Coef))) +
ggtitle("Model coefficients (MC)") +
theme_bw(base_size = 15) +
theme(strip.background = element_blank(),
strip.placement = "outside",
strip.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
axis.title.y = element_blank()
) +
scale_fill_manual(name = "Effect direction",
labels = c("Advancing",
"Unimportant",
"Delaying"),
values = c("-1" = "red",
"0" = "grey",
"1" = "dark green")) +
ylab("PLS coefficient") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
library(patchwork)
plot <- (VIP_plot +
coeff_plot +
temp_plot +
plot_layout(ncol = 1,
guides = "collect")
) & theme(legend.position = "right",
legend.text = element_text(size = 8),
legend.title = element_text(size = 10),
axis.title.x = element_blank())
plot
}plot_PLS_chill_force(plscf,
chill_metric = "Chill_Portions",
heat_metric = "GDH",
chill_label = "CP",
heat_label = "GDH",
chill_phase = c(-48, 62),
heat_phase = c(3, 105.5))
chill_phase <- c(317, 62)
heat_phase <- c(3, 105.5)
chill <- tempResponse(hourtemps = temps_hourly,
Start_JDay = chill_phase[1],
End_JDay = chill_phase[2],
models = list(Chill_Portions = Dynamic_Model),
misstolerance = 10)
heat <- tempResponse(hourtemps = temps_hourly,
Start_JDay = heat_phase[1],
End_JDay = heat_phase[2],
models = list(GDH = GDH))
ggplot(data = chill,
aes(x = Chill_Portions)) +
geom_histogram() +
ggtitle("Chill accumulation during endodormancy (Chill Portions)") +
xlab("Chill accumulation (Chill Portions)") +
ylab("Frequency between 1958 and 2019") +
theme_bw(base_size = 12)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data = heat,
aes(x = GDH)) +
geom_histogram() +
ggtitle("Heat accumulation during ecodormancy (GDH)") +
xlab("Heat accumulation (Growing Degree Hours)") +
ylab("Frequency between 1958 and 2019") +
theme_bw(base_size = 12)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
chill_requirement <- mean(chill$Chill_Portions)
chill_req_error <- sd(chill$Chill_Portions)
heat_requirement <- mean(heat$GDH)
heat_req_error <- sd(heat$GDH)mean_temp_period <- function(
temps,
start_JDay,
end_JDay,
end_season = end_JDay)
{ temps_JDay <- make_JDay(temps) %>%
mutate(Season =Year)
if(start_JDay > end_season)
temps_JDay$Season[which(temps_JDay$JDay >= start_JDay)]<-
temps_JDay$Year[which(temps_JDay$JDay >= start_JDay)]+1
if(start_JDay > end_season)
sub_temps <- subset(temps_JDay,
JDay <= end_JDay | JDay >= start_JDay)
if(start_JDay <= end_JDay)
sub_temps <- subset(temps_JDay,
JDay <= end_JDay & JDay >= start_JDay)
mean_temps <- aggregate(sub_temps[, c("Tmin", "Tmax")],
by = list(sub_temps$Season),
FUN = function(x) mean(x,
na.rm=TRUE))
mean_temps[, "n_days"] <- aggregate(sub_temps[, "Tmin"],
by = list(sub_temps$Season),
FUN = length)[,2]
mean_temps[, "Tmean"] <- (mean_temps$Tmin + mean_temps$Tmax) / 2
mean_temps <- mean_temps[, c(1, 4, 2, 3, 5)]
colnames(mean_temps)[1] <- "End_year"
return(mean_temps)
}
mean_temp_chill <- mean_temp_period(temps = temps,
start_JDay = chill_phase[1],
end_JDay = chill_phase[2],
end_season = 60)
mean_temp_heat <- mean_temp_period(temps = temps,
start_JDay = heat_phase[1],
end_JDay = heat_phase[2],
end_season = 60)mean_temp_chill <-
mean_temp_chill[which(mean_temp_chill$n_days >=
max(mean_temp_chill$n_days)-1),]
mean_temp_heat <-
mean_temp_heat[which(mean_temp_heat$n_days >=
max(mean_temp_heat$n_days)-1),]
mean_chill <- mean_temp_chill[, c("End_year",
"Tmean")]
colnames(mean_chill)[2] <- "Tmean_chill"
mean_heat <- mean_temp_heat[,c("End_year",
"Tmean")]
colnames(mean_heat)[2] <- "Tmean_heat"
phase_Tmeans <- merge(mean_chill,
mean_heat,
by = "End_year")
pheno <- roter_B
colnames(pheno)[1] <- "End_year"
Tmeans_pheno <- merge(phase_Tmeans,
pheno,
by = "End_year")
head(Tmeans_pheno)## End_year Tmean_chill Tmean_heat pheno
## 1 1959 2.924324 4.5815534 104
## 2 1960 3.147206 4.5100953 115
## 3 1961 4.047768 6.0223301 99
## 4 1962 2.541892 2.6927184 134
## 5 1963 -3.529730 -0.9257282 127
## 6 1964 1.443243 2.4723301 126library(fields)## Lade nötiges Paket: spam## Spam version 2.11-0 (2024-10-03) is loaded.
## Type 'help( Spam)' or 'demo( spam)' for a short introduction
## and overview of this package.
## Help for individual functions is also obtained by adding the
## suffix '.spam' to the function name, e.g. 'help( chol.spam)'.##
## Attache Paket: 'spam'## Die folgenden Objekte sind maskiert von 'package:base':
##
## backsolve, forwardsolve## Lade nötiges Paket: viridisLite##
## Try help(fields) to get started.k <- Krig(x = as.matrix(
Tmeans_pheno[,
c("Tmean_chill",
"Tmean_heat")]),
Y = Tmeans_pheno$pheno)
pred <- predictSurface(k)
colnames(pred$z) <- pred$y
rownames(pred$z) <- pred$x
library(reshape2)##
## Attache Paket: 'reshape2'## Das folgende Objekt ist maskiert 'package:tidyr':
##
## smithsmelted <- melt(pred$z)
library(metR)## Warning: Paket 'metR' wurde unter R Version 4.4.2 erstellt##
## Attache Paket: 'metR'## Das folgende Objekt ist maskiert 'package:purrr':
##
## crosslibrary(colorRamps)
colnames(melted) <- c("Tmean_chill",
"Tmean_heat",
"value")
ggplot(melted,
aes(x = Tmean_chill,
y = Tmean_heat,
z = value)) +
geom_contour_fill(bins = 100) +
scale_fill_gradientn(colours = alpha(matlab.like(15)),
name = "Bloom date \n(day of the year)") +
geom_contour(col = "black") +
geom_point(data = Tmeans_pheno,
aes(x = Tmean_chill,
y = Tmean_heat,
z = NULL),
size = 0.7) +
geom_text_contour(stroke = 0.2) +
ylab(expression(paste("Forcing phase ",
T[mean],
" (",
degree,
"C)"))) +
xlab(expression(paste("Chilling phase ",
T[mean],
" (",
degree,
"C)"))) +
theme_bw(base_size = 15)## Warning: Removed 4368 rows containing non-finite outside the scale range
## (`stat_contour()`).
temps <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/data ch20-25/TMaxTMin1958-2019_patched.csv")
roter_B <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/data ch20-25/Roter_Boskoop_bloom_1958_2019.csv") %>%
select(Pheno_year, First_bloom) %>%
mutate(Year = as.numeric(substr(First_bloom, 1, 4)),
Month = as.numeric(substr(First_bloom, 5, 6)),
Day = as.numeric(substr(First_bloom, 7, 8))) %>%
make_JDay() %>%
select(Pheno_year,
JDay) %>%
rename(Year = Pheno_year,
pheno = JDay)
colnames(roter_B) <- c("Year", "pheno")
Cali_temps_hourly <- stack_hourly_temps(temps,
latitude = 50.6)
Cali_daychill <- daily_chill(hourtemps = Cali_temps_hourly,
running_mean = 1,
models = list(Chilling_Hours = Chilling_Hours,
Utah_Chill_Units = Utah_Model,
Chill_Portions = Dynamic_Model,
GDH = GDH)
)
plscf <- PLS_chill_force(daily_chill_obj = Cali_daychill,
bio_data_frame = roter_B,
split_month = 6,
chill_models = "Chill_Portions",
heat_models = "GDH",
runn_means = 11)
plot_PLS_chill_force(plscf,
chill_metric = "Chill_Portions",
heat_metric = "GDH",
chill_label = "CP",
heat_label = "GDH",
chill_phase = c(-56, 5),
heat_phase = c(19, 77))
- We’ve looked at data from a number of locations so far. How would
you expect this surface plot to look like in Beijing? And how should it
look in Tunisia?
- Beijing
- The chilling period (x-axis) will have much colder temperatures than Klein-Altendorf.
- The forcing period (y-axis) will be more narrow than Klein-Altendorf.
- The separation lines for the bloom dates will be horizontal.
- The forcing period will mainly influence the bloom date.
- Tunisia
- The chilling period (x-axis) will have warmer temperatures than Klein-Altendorf.
- The forcing period (y-axis) will be a lot wider than Klein-Altendorf.
- The separation lines for the bloom dates will be nearly vertical.
- The chilling period will mainly influence the bloom date.
- Beijing
Chapter 27The relative importance of chill and heat
Describe the temperature response hypothesis outlined in this chapter.
The temperature response hypothesis explains how temperature influences tree phenology through chilling and forcing effects. Chilling temperatures are required to release bud dormancy and forcing temperatures to drive bud development and growth. The hypothesis examines how the balance between chilling and forcing temperatures determines the timing of phenological events. Tree species and even single varieties have specific needs in chilling and forcing to produce sufficient fruits.
In the graphic below is a representation how the relationship of chilling and forcing influences the bloom date of trees.
Graphical representation of the temperature
response hypothesis
Chapter 28 Experimentally enhanced PLS
There are no tasks for this chapter.
Chapter 29
- Explain the difference between output validation and process validation.
Output validation
Compares the model’s outputs to real-world observations to assess accuracy
The focus is on the end results that the model produces
Process validation
Looks into the internal mechanisms and assumptions of the model
The focus is on the model’s processes and if they correctly represent real-world systems.
So rather than validating only the model’s results it checks if the path to get the results aligns with reality
- Explain what a validity domain is and why it is important to consider this whenever we want to use our model to forecast something.
Validity domain
Defines the boundaries within which the model works in a useful way and delivers reliable results.
Importance
Ensures that the model is applied only to situations where it has been validated and prevents misuse.
What is validation for purpose?
Assesses if a model is suitable for a specific intended application. It checks if the model meets the requirements and objectives of the intended use. Also the accuracy of the model and its relevance to the specific context are evaluated.
How can we ensure that our model is suitable for the predictions we want to make?
To ensure this there are some steps that need to be followed:
Define the models purpose and clearly outline the specific objectives and applications of the desired model.
Validate your chosen model. For this use both output and process validation to assess accuracy and internal consistency.
Determine the model’s valid range. Identify the conditions under which the model operates reliably.
Do continuous evaluations of the model to ensure its accuracy. Compare the model’s predictions to real-world data and update the model if errors occur.
Chapter 30 The PhenoFlex Model
- Parameterize the PhenoFlex model for `Roter Boskoop’ apples.
Boskoop <-
read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/Roter_Boskoop_bloom_1958_2019.csv") %>%
select(Pheno_year, First_bloom) %>%
mutate(Year = as.numeric(substr(First_bloom, 1, 4)),
Month = as.numeric(substr(First_bloom, 5, 6)),
Day = as.numeric(substr(First_bloom, 7, 8))) %>%
make_JDay() %>%
select(Pheno_year, JDay) %>%
rename(Year = Pheno_year,
pheno = JDay)
hourtemps <-
read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/TMaxTMin1958-2019_patched.csv") %>%
stack_hourly_temps(latitude = 50.6)
par <- c(40, 190, 0.5, 25, 3372.8, 9900.3, 6319.5, 5.939917e13, 4, 36, 4, 1.60)
upper <- c(41, 200, 1.0, 30, 4000.0, 10000.0, 7000.0, 6.e13, 10, 40, 10, 50.00)
lower <- c(38, 180, 0.1, 0 , 3000.0, 9000.0, 6000.0, 5.e13, 0, 0, 0, 0.05)
SeasonList <- genSeasonList(hourtemps$hourtemps,
mrange = c(8, 6),
years = c(1959:2019))Fit_res <- phenologyFitter(par.guess = par,
modelfn = PhenoFlex_GDHwrapper,
bloomJDays = Boskoop$pheno[which(Boskoop$Year > 1957)],
SeasonList = SeasonList,
lower = lower,
upper = upper,
control = list(smooth = FALSE,
verbose = FALSE,
maxit = 1000,
nb.stop.improvement = 5))
Boskoop_par <- Fit_res$par
write.csv(Boskoop_par, "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/PhenoFlex_parameters_roter_Boskoop.csv")- Produce plots of predicted vs. observed bloom dates and distribution of prediction errors.
Boskoop_par <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/PhenoFlex_parameters_roter_Boskoop.csv")[,2]
SeasonList <- genSeasonList(hourtemps$hourtemps,
mrange = c(8, 6),
years = c(1959:2019))
Boskoop_PhenoFlex_predictions <- Boskoop[which(Boskoop$Year > 1958),]
for(y in 1:length(Boskoop_PhenoFlex_predictions$Year))
Boskoop_PhenoFlex_predictions$predicted[y] <-
PhenoFlex_GDHwrapper(SeasonList[[y]],
Boskoop_par)
Boskoop_PhenoFlex_predictions$Error <-
Boskoop_PhenoFlex_predictions$predicted -
Boskoop_PhenoFlex_predictions$pheno
ggplot(Boskoop_PhenoFlex_predictions,
aes(x = pheno,
y = predicted)) +
geom_point() +
geom_abline(intercept = 0,
slope = 1) +
theme_bw(base_size = 15) +
xlab("Observed bloom date (Day of the year)") +
ylab("Predicted bloom date (Day of the year)") +
ggtitle("Predicted vs. observed bloom dates")
ggplot(Boskoop_PhenoFlex_predictions,
aes(Error)) +
geom_histogram() +
ggtitle("Distribution of prediction errors")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
- Compute the model performance metrics RMSEP, mean error and mean absolute error.
RMSEP(Boskoop_PhenoFlex_predictions$predicted,
Boskoop_PhenoFlex_predictions$pheno)## [1] 4.52457mean(Boskoop_PhenoFlex_predictions$Error)## [1] -1.23125mean(abs(Boskoop_PhenoFlex_predictions$Error))## [1] 3.347917Chapter 31 The PhenoFlex Model - a second look
- Make chill and heat response plots for the ‘Roter Boskoop’ PhenoFlex model for the location you did the earlier analyses for.
I really tried to incorporate my own temperature data, but I wasn’t able to fin out how to fix the code to the shorter time span (1990-2019 instead of 1958-2019). Even if I got the bloomJDays andSeasonList to the same length I still got error messages that there are some statements not fully met. So Iswitched back to the CKA temperatures to be able to create the plots correctly.
Boskoop <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/Roter_Boskoop_bloom_1958_2019.csv") %>%
select(Pheno_year, First_bloom) %>%
mutate(Year = as.numeric(substr(First_bloom, 1, 4)),
Month = as.numeric(substr(First_bloom, 5, 6)),
Day = as.numeric(substr(First_bloom, 7, 8))) %>%
make_JDay() %>%
select(Pheno_year, JDay) %>%
rename(Year = Pheno_year,
pheno = JDay)
temp_data <- read.csv("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/TMaxTMin1958-2019_patched.csv") %>% stack_hourly_temps(latitude = 50.6)
par <- c(40, 190, 0.5, 25, 3372.8, 9900.3, 6319.5, 5.939917e13, 4, 36, 4, 1.60)
upper <- c(41, 200, 1.0, 30, 4000.0, 10000.0, 7000.0, 6.e13, 10, 40, 10, 50.00)
lower <- c(38, 180, 0.1, 0 , 3000.0, 9000.0, 6000.0, 5.e13, 0, 0, 0, 0.05)
SeasonList <- genSeasonList(temp_data$temp_data,
mrange = c(8, 6),
years = c(1959:2019))
Fit_res <- phenologyFitter(par.guess = par,
modelfn = PhenoFlex_GDHwrapper,
bloomJDays = Boskoop$pheno[which(Boskoop$Year > 1957)],
SeasonList = SeasonList,
lower = lower,
upper = upper,
control = list(smooth = FALSE,
verbose = FALSE,
maxit = 1000,
nb.stop.improvement = 5))
Boskoop_par <- Fit_res$par
write.csv(Boskoop_par, "D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/PhenoFlex_parameters_roter_Boskoop.csv")apply_const_temp <-
function(temp, A0, A1, E0, E1, Tf, slope, portions = 1200, deg_celsius = TRUE)
{
temp_vector <- rep(temp,
times = portions)
res <- chillR::DynModel_driver(temp = temp_vector,
A0 = A0,
A1 = A1,
E0 = E0,
E1 = E1,
Tf = Tf,
slope = slope,
deg_celsius = deg_celsius)
return(res$y[length(res$y)])
}
gen_bell <- function(par,
temp_values = seq(-5, 20, 0.1)) {
E0 <- par[5]
E1 <- par[6]
A0 <- par[7]
A1 <- par[8]
Tf <- par[9]
slope <- par[12]
y <- c()
for(i in seq_along(temp_values)) {
y[i] <- apply_const_temp(temp = temp_values[i],
A0 = A0,
A1 = A1,
E0 = E0,
E1 = E1,
Tf = Tf,
slope = slope)
}
return(invisible(y))
}
GDH_response <- function(T, par)
{Tb <- par[11]
Tu <- par[4]
Tc <- par[10]
GDH_weight <- rep(0, length(T))
GDH_weight[which(T >= Tb & T <= Tu)] <-
1/2 * (1 + cos(pi + pi * (T[which(T >= Tb & T <= Tu)] - Tb)/(Tu - Tb)))
GDH_weight[which(T > Tu & T <= Tc)] <-
(1 + cos(pi/2 + pi/2 * (T[which(T > Tu & T <= Tc)] -Tu)/(Tc - Tu)))
return(GDH_weight)
}Boskoop <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/PhenoFlex_parameters_roter_Boskoop.csv")[,2]
temp_values = seq(-5, 30, 0.1)
temp_response <- data.frame(Temperature = temp_values,
Chill_response = gen_bell(Boskoop,
temp_values),
Heat_response = GDH_response(temp_values,
Boskoop))
pivoted_response <- pivot_longer(temp_response,
c(Chill_response,
Heat_response))
ggplot(pivoted_response,
aes(x = Temperature,
y = value)) +
geom_line(linewidth = 2,
aes(col = name)) +
ylab("Temperature response (arbitrary units)") +
xlab("Temperature (°C)") +
facet_wrap(vars(name),
scales = "free",
labeller =
labeller(name = c(Chill_response = c("Chill response"),
Heat_response = c("Heat response")))) +
scale_color_manual(values = c("Chill_response" = "blue",
"Heat_response" = "red")) +
theme_bw(base_size = 15) +
theme(legend.position = "none")
Chapter 32 Can we improve the performance of PhenoFlex?
What was the objective of this work?
The study aimed to enhance the performance of the PhenoFlex model by refining its calibration process.
What was the main conclusion?
That the PhenoFlex model’s performance improved when using a calibration approach that incorporated the standard deviation and the mean absolute error of phenological dates.
What experiments could we conduct to test the hypothesis that emerged at the end of the conclusion?
Cross-validation
Apply the model to diverse datasets (different geographic regions and spiecies) to test the robustness and generalizability of the PhenoFlex Model.
Use the new data to test the calibrations that were done with the already present data.
Comparative analysis
- Compare the predictive accuracy of the PhenoFlex model calibrated with the combined approach against models calibrated with only one of the error metrics to evaluate the performance differences.
Sensitivity analysis
- Investigate how variations in weighting between standard deviation and mean absolute error during calibration affect the model’s predictions to determine optimal calibration settings.
Longitudinal studies
- Monitor the model’s predictive performance over multiple years to ensure that the improved calibration method maintains its accuracy over time.
Chapter 33 Frost risk analysis
- Illustrate the development of the bloom period over the duration of the weather record. Use multiple ways to show this - feel free to be creative.
CKA_Boskoop <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/Roter_Boskoop_bloom_1958_2019.csv")
CKA_weather <- read_tab("D:/! Ricardas Daten/Studium/Master/03. Semester (WiSe 24-25)/Tree phenology analysis in R/Logbook chapters/TMaxTMin1958-2019_patched.csv")
CKA_Boskoop <-
CKA_Boskoop %>%
pivot_longer(cols = "First_bloom":"Last_bloom",
names_to = "variable",
values_to="YEARMODA") %>%
mutate(Year = as.numeric(substr(YEARMODA, 1, 4)),
Month = as.numeric(substr(YEARMODA, 5, 6)),
Day = as.numeric(substr(YEARMODA, 7, 8))) %>%
make_JDay() ggplot(data = CKA_Boskoop,
aes(Pheno_year,
JDay,
col = variable)) +
geom_line() +
theme_bw(base_size = 15) +
scale_color_discrete(
name = "Phenological event",
labels = c("First bloom",
"Full bloom",
"Last bloom")) +
xlab("Phenological year") +
ylab("Julian date (day of the year)")
ggplot(data = CKA_Boskoop,
aes(Pheno_year,
JDay,
col = variable)) +
geom_line() +
theme_bw(base_size = 15) +
scale_color_discrete(name = "Phenological event",
labels = c("First bloom",
"Full bloom",
"Last bloom")) +
xlab("Phenological year") +
ylab("Julian date (day of the year)") +
geom_smooth(method = "lm")## `geom_smooth()` using formula = 'y ~ x'
ggplot(data=CKA_Boskoop,aes(Pheno_year,JDay,col=variable)) +
geom_smooth() +
theme_bw(base_size=15) +
scale_color_discrete(
name = "Phenological event",
labels = c("First bloom", "Full bloom", "Last bloom")) +
xlab("Phenological year") +
ylab("Julian date (day of the year)") ## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
- Evaluate the occurrence of frost events at Klein-Altendorf since 1958. Illustrate this in a plot.
require(Kendall)
Kendall_first <-
Kendall(x = CKA_Boskoop$Pheno_year[
which(CKA_Boskoop$variable == "First_bloom")],
y = CKA_Boskoop$JDay[
which(CKA_Boskoop$variable == "First_bloom")])
Kendall_full <-
Kendall(x = CKA_Boskoop$Pheno_year[
which(CKA_Boskoop$variable == "Full_bloom")],
y = CKA_Boskoop$JDay[
which(CKA_Boskoop$variable == "Full_bloom")])
Kendall_last <-
Kendall(x = CKA_Boskoop$Pheno_year[
which(CKA_Boskoop$variable == "Last_bloom")],
y = CKA_Boskoop$JDay[
which(CKA_Boskoop$variable == "Last_bloom")])
Kendall_first## tau = -0.35, 2-sided pvalue =0.000084584Kendall_full## tau = -0.447, 2-sided pvalue =0.00000046775Kendall_last## tau = -0.38, 2-sided pvalue =0.000019136The p-values are all smaller than 0.05, so it is pretty clear that there is a trend.
frost_df = data.frame(
lower = c(-1000, 0),
upper = c(0, 1000),
weight = c(1, 0))
frost_model <- function(x) step_model(x,
frost_df)
hourly <- stack_hourly_temps(CKA_weather,
latitude = 50.625)
frost <- tempResponse(hourly,
models = c(frost = frost_model))
ggplot(frost,
aes(End_year,
frost)) +
geom_smooth() +
geom_point() +
ylim(c(0, NA)) +
ylab("Frost hours per year") +
xlab("Year")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
- Produce an illustration of the relationship between spring frost events and the bloom period of ‘Roter Boskoop’.
frost_model_no_summ <-
function(x) step_model(x,
frost_df,
summ=FALSE)
hourly$hourtemps[, "frost"] <- frost_model_no_summ(hourly$hourtemps$Temp)
Daily_frost_hours <- aggregate(hourly$hourtemps$frost,
by = list(hourly$hourtemps$YEARMODA),
FUN = sum)
Daily_frost <- make_JDay(CKA_weather)
Daily_frost[, "Frost_hours"] <- Daily_frost_hours$x
Daily_frost$Frost_hours[which(Daily_frost$Frost_hours == 0)] <- NA
Ribbon_Boskoop <-
CKA_Boskoop %>%
select(Pheno_year, variable, JDay) %>%
pivot_wider(names_from = "variable", values_from = "JDay")
lookup_dates <- Ribbon_Boskoop
row.names(lookup_dates) <- lookup_dates$Pheno_year
Daily_frost[, "First_bloom"]<-
lookup_dates[as.character(Daily_frost$Year),
"First_bloom"]
Daily_frost[, "Last_bloom"]<-
lookup_dates[as.character(Daily_frost$Year),
"Last_bloom"]
Daily_frost[which(!is.na(Daily_frost$Frost_hours)),
"Bloom_frost"] <-
"Before bloom"
Daily_frost[which(Daily_frost$JDay >= Daily_frost$First_bloom),
"Bloom_frost"]<-
"During bloom"
Daily_frost[which(Daily_frost$JDay > Daily_frost$Last_bloom),
"Bloom_frost"]<-
"After bloom"
Daily_frost[which(Daily_frost$JDay > 180),
"Bloom_frost"]<-
"Before bloom"
ggplot(data = Ribbon_Boskoop,
aes(Pheno_year)) +
geom_ribbon(aes(ymin = First_bloom,
ymax = Last_bloom),
fill = "light gray") +
geom_line(aes(y = Full_bloom)) +
theme_bw(base_size = 15) +
xlab("Phenological year") +
ylab("Julian date (day of the year)") +
geom_point(data = Daily_frost,
aes(Year,
JDay,
size = Frost_hours,
col = Bloom_frost),
alpha = 0.8) +
scale_size(range = c(0, 5),
breaks = c(1, 5, 10, 15, 20),
labels = c("1", "5", "10", "15", "20"),
name = "Frost hours") +
scale_color_manual(
breaks = c("Before bloom",
"During bloom",
"After bloom"),
values = c("light green",
"red",
"light blue"),
name = "Frost timing") +
theme_bw(base_size = 15) +
ylim(c(75, 140))
Evaluate how the risk of spring frost for this cultivar has changed over time. Has there been a significant trend?
In the viewed period is a trend visible that shows earlier bloom dates, which could increase the risk of frost damage. However, there is no higher greater frost damage risk visible in the data.
Chapter 34 A robust method to estimate future frost risks (?)
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Chapter 35 Major concepts
There are no tasks for this chapter.